Parametric models are ubiquitous in science, and the inferred parameters of those models fit to data can be used to gain insight into the underlying data generation process. Union of Intersections is a novel statistical-machine learning framework that enables improved feature-selection and estimation capabilities in diverse parametric models fit to data. Together, these enhanced inference capabilities lead to improved predictive performance and model interpretability.

Led by Kristofer Bouchard

Natural systems are dynamic systems. Often, scientists want to uncover dynamic low-dimensional latent structures embedded in high-dimensional noisy observations. However, most unsupervised learning methods are focused on capturing variance, which may not be related to dynamics. We have developed novel dimensionality-reduction methods that optimize an objective function directly designed for dynamic data (predictive information between the past-future of a time series). We have created two methods based on maximizing predictive information: a linear dimensionality reduction method (Dynamical Components Analysis, or DCA) and non-linear Compressed Predictive Information Coding (CPIC).

Led by Kristofer Bouchard

Optimization, a key tool in machine learning and statistics, relies on regularization to reduce overfitting. Traditional regularization methods control a norm of the solution to ensure its smoothness. We propose a method that instead builds on insights from topological data analysis. This approach enables a faster and more precise topological regularization, the benefits of which we illustrate with experimental evidence. Furthermore, we extend our method to more general topological losses.

Led by Dmitriy Morozov

The Produced Water Application for Beneficial Reuse, Environmental Impact and Treatment Optimization (PARETO) is specifically designed for produced water management and beneficial reuse. The major deliverable of this project will be an open-source, optimization-based, downloadable and executable produced water decision-support application, PARETO, that can be run by upstream operators, midstream companies, technology providers, water end users, research organizations, and regulators.

Led by Dan Gunter

We introduced approximate Lorentz equivariant neural network (NN) architectures to address key high-energy physics (HEP) data processing tasks. Symmetries arising from physical laws like Einstein's special relativity can improve the expressiveness, interpretability, and resource efficiency of NNs.

Led by Xiangyang Ju

The prior probability density functions of Gaussian processes are entirely defined by a covariance or kernel function and a prior mean function. Chosen correctly, the kernel function has the capability to constrain the solution space to only contain functions with certain domain-knowledge-adhering properties. Furthermore, the training itself can be formulated as a constrained optimization problem. gpCAM, our python software package for autonomous experimentation and Gaussian-process function approximation, is tailored to allow the user to inject physical knowledge via the kernel and the prior mean function. The solutions are significantly more accurate, with more realistically-estimated uncertainties, and can approximate functions using fewer data points.

Led by Marcus Noack

We use Normalizing Flows to develop fast Bayesian statistical analysis methods for scientific data analysis that can be applied to a wide range of scientific domains and problems. The methods can be used for posterior sampling and global optimization applications, with or without gradient information. Recent examples are DLMC (Deterministic Langevin Monte Carlo) and Preconditioned Monte Carlo (PocoMC).

Led by Uros Seljak.

Water treatment, as a sophisticated environmental and chemical engineering practice, designs physical/chemical/biological processes to produce clean water. We provide computational and modeling solutions to optimize the performance, energy use, and economic cost of existing and developing water treatment processes and infrastructures. Conventional linear and nonlinear programming are applied to theory- and data-informed equation systems describing the engineering systems at different scales. We ultimately deliver the optimization efficacy as user-friendly and open-source software. We explore the potential advantages of novel ML and AI algorithms to complement conventional numerical optimization approaches, tackling complexity and dynamics challenges in broad water systems.

Led by Dan Gunter

The IDAES integrated platform (IDAES-IP) brings the most advanced modeling and optimization capabilities to challenges around the reliable, environmentally sustainable, and cost-efficient transformation and decarbonization of the world’s energy systems. IDAES utilizes state-of-the-art equation-oriented optimization solvers and algorithms to enable the design, optimization, and operation of complex, innovative steady state and dynamic processes.

Led by Dan Gunter

4DCamera Distillery is a program that will develop and deploy methods and tools based on AI and ML to analyze electron scattering information from the data streams of fast direct electron detectors. The team behind the effort, composed of researchers from Brookhaven National Laboratory, Oak Ridge National Laboratory, Argonne National Laboratory, Sandia National Laboratory, and Los Alamos National Laboratory, as well as Berkeley Lab, will address both the critical need for data reduction tools for these detectors and capitalize on the scientific opportunities to create new modes of measurement and experimentation that are enabled by fast electron detection.

Led by Andrew Minor, Colin Ophus

Flash droughts come on seemingly without warning, and sometimes with devastating effects. In this NSF PREEVENTS project, we are working to advance the understanding and subseasonal-to-seasonal prediction of flash droughts and their associated heat extremes. We are developing machine-learning-based estimates of photosynthesis, evapotranspiration, and respiration at high resolution, in combination with remotely sensed estimates of evaporative stress, to define and characterize flash drought.

Led by Trevor Keenan

HGDL is an optimization algorithm specialized in finding not only one but a diverse set of optima, alleviating challenges of non-uniqueness that are common in modern applications such as inversion problems and training of machine learning models. HGDL is customized for distributed high-performance computing; all workers can be distributed across as many nodes or cores. All local optimizations will then be executed in parallel. As solutions are found, they are deflated which effectively removes those optima from the function, so that they cannot be re-identified by subsequent local searches.

Led by Paolo Califiura

Living systems create a wide range of biomolecular arrays with complex functions and exquisite organization. This has inspired synthetic equivalents for a range of applications, enabling new high-throughput approaches to biocomposites, diagnostics, and materials research. This study with data analyses helps broaden the physical understanding of biomolecular assembly by tracking motion at unprecedented resolution and defining a general procedure for using in situ visualization and machine learning to explore such dynamics. This research characterizes the “energy landscape” for protein orientation and, by analyzing the motion of the proteins, shows how that energy landscape controls the rate of motion between different orientations.

Led by Oliver Rübel

Mobiliti is a traffic simulator for large-scale transportation networks, enabling users to model dynamic congestion and vehicle rerouting behavior in response to hypothetical traffic and network events. We are currently exploring the use of deep reinforcement learning along with graphical models (RL and DCRNNs) to predict traffic, optimize traffic signal controllers, and quantify their impact on system-level congestion.

Led by Cy Chan

Reconstructing the trajectories of thousands of charged particles from a collision event as they fly through a HEP detector is a combinatorially hard pattern recognition problem. Exa.TrkX, a DOE CompHEP project and a collaboration of data scientists and computational physicists from the ATLAS, CMS, and DUNE experiments, is developing Graph Neural Network models aimed at reconstructing millions of particle trajectories per second from Petabytes of raw data produced by the next generation of detectors at the Energy and Intensity Frontiers. Exa.TrkX is also exploring the scaling of distributed training of GNN models on DOE pre-exascale systems and deploying GNN models with microsecond latencies on FPGA-based real-time processing systems.

Led by Paolo Califiura

We use persistent homology to describe the geometry of nanoporous materials at various scales. We combine our topological descriptor with traditional structural features and investigate the relative importance of each to the prediction tasks. Our results not only show a considerable improvement compared to the baseline, but they also highlight that topological features capture information complementary to the structural features. Furthermore, by investigating the importance of individual topological features in the model, we are able to pinpoint the location of the relevant pores, contributing to our atom-level understanding of structure-property relationships.

Led by Dmitriy Morozov

Three machine learning techniques (multilayer perceptron, random forest, and Gaussian process) provide fast surrogate models for lower hybrid current drive (LHCD) simulations. A single GENRAY/CQL3D simulation without radial diffusion of fast electrons requires several minutes of wall-clock time to complete, which is acceptable for many purposes, but too slow for integrated modeling and real-time control applications. The machine learning models use a database of more than 16,000 GENRAY/CQL3D simulations for training, validation, and testing. Latin hypercube sampling methods ensure that the database covers the range of nine input parameters with sufficient density in all regions of parameter space. The surrogate models reduce the inference time from minutes to milliseconds with high accuracy across the input parameter space.

Led by Talita Perciano, Zhe Bai

Persistent cohomology is a powerful technique for discovering topological structure in data. Strategies for its use in neuroscience are still undergoing development. We comprehensively and rigorously assess its performance in simulated neural recordings of the brain's spatial representation system. Our results reveal how dataset parameters affect the success of topological discovery and suggest principles for applying persistent cohomology to experimental neural recordings.

Led by Dmitriy Morozov

Cosmological probes pose an inverse problem where the measurement result is obtained through observations, and the objective is to infer values of model parameters that characterize the underlying physical system—our universe, from these observations and theoretical forward-modeling. The only way to accurately forward-model physical behavior on small scales is via expensive numerical simulations, which are further "emulated" due to their high cost. Emulators are commonly built with a set of simulations covering the parameter space; the aim is to establish an approximately constant prediction error across the hypercube. We provide a description of a novel statistical framework for obtaining accurate parameter constraints. The proposed framework uses multi-output Gaussian process emulators that are adaptively constructed using Bayesian optimization methods with the goal of maintaining a low emulation error in the region of the hypercube preferred by the observational data. We compare several approaches for constructing multi-output emulators that enable us to take possible inter-output correlations into account while maintaining the efficiency needed for inference.

Led by Dmitriy Morozov, Zarija Lukić

Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity. The rich, unlabeled image data from such surveys can be used to develop powerful self-supervised AI models that distill low-dimensional representations which are robust to symmetries, uncertainties, and noise in each image; these semantically meaningful representations make the self-supervised model immediately useful for downstream tasks like morphology classification, redshift estimation, similarity search, and detection of rare events, paving new pathways for scientific discovery.

Led by Peter Harrington

In an ever-demanding world for zero-emission clean energy sources, vehicle electrification will bring major contributions as each clean car that substitutes one based on fossil fuel could save 1.5 tons of carbon dioxide per year. To expand the e-vehicle fleet, new solutions to store energy must deliver lighter, longer ranges, and more powerful energy batteries, such as solid-state lithium metal batteries (LMB). Different from traditional lithium-ion, LMB uses solid electrodes and electrolytes, providing superior electrochemical performance and high energy density. One of the challenges of this new technology is to predict cycling stability and prevent the formation of lithium dendrite growth. These morphologies are key to the LMB quality, and they can be captured and analyzed using X-ray microtomography. This project focuses on new deep learning based on U-net, Y-net, and viTransformers for detection and segmentation of defects in LMB.

Led by Dani Ushizima

PySMO is an open-source tool for generating accurate algebraic surrogates that are directly integrated with an equation-oriented (EO) optimization platform, specifically IDAES and its underlying optimization library, Pyomo. PySMO includes implementations of several sampling and surrogate methods (polynomial regression, Kriging, and RBFs), providing a breadth of capabilities suitable for a variety of engineering applications. PySMO surrogates have been demonstrated to be very useful for enabling the algebraic representation of external simulation codes, black-box models, and complex phenomena in IDAES and other related projects.

Led by Oluwamayowa Amusat

Multi-physics cosmological simulations are powerful tools for studying the formation and evolution of structure in the universe but require extreme computational resources. In particular, modeling the hydrodynamic interactions of baryonic matter adds significant expense but is required to accurately capture small-scale phenomena and create realistic mock-skies for key observables. This project uses deep neural networks to reconstruct important hydrodynamical quantities from coarse or N-body-only simulations, vastly reducing the amount of compute resources required to generate high-fidelity realizations while still providing accurate estimates with realistic statistical properties.

Led by Peter Harrington

We developed a neural network-based surrogate model for simulating the process whereby partons are converted to hadrons for high energy physics. The development is the first step towards a fully data-driven neural network-based hadronization simulator.

Led by Xiangyang Ju

The Perlmutter system is a world-leading AI supercomputer consisting of over 6,000 Nvidia A100 GPUs, an all-flash filesystem, and a novel high-speed network. The National Energy Research Scientific Computing Center (NERSC) at Berkeley Lab also works closely with vendors  to ensure optimized software for AI at large computing scale, as well as consulting, joint projects, and training to enable the community to exploit these resources.

Led by Steven Farrell

FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at high resolution. FourCastNet accurately predicts high-resolution, fast-timescale variables such as the surface wind speed, precipitation, and atmospheric water vapor. It can generate forecasts with extreme computational savings compared to standard numerical weather prediction models. It has important implications for planning wind energy resources and predicting extreme weather events such as tropical cyclones, extra-tropical cyclones, and atmospheric rivers.

Led by Shashank Subramanian

MLPerf HPC is a machine learning performance benchmark suite for scientific ML workloads on large supercomputers. It measures the time to train deep learning models on massive scientific datasets as well as full system scale throughput for training many models concurrently. MLPerf HPC has had two successful annual submission rounds featuring results on systems around the world, including the Perlmutter system at NERSC.

Led by Steven Farrell

The power distribution grid, like many cyber-physical systems, was developed with careful consideration for safe operation, but a number of features of the power system make it particularly vulnerable to cyber attacks via IP networks. The goal of this project was to design and implement a measurement network that can detect and report the resultant impact of cyber security attacks on the distribution system network. The result is a system that provides an independent, integrated picture of the distribution grid’s physical state, which is difficult for a cyber-attacker to subvert using data-spoofing techniques.

Led by Sean Peisert

This project uses complex process simulation models for advanced biofuel and bioproduct production to develop and train machine learning (ML)-based surrogate models. Researchers need flexibility to explore different scenarios and understand how their work may impact upstream and downstream processes, as well as cost and greenhouse gas emissions. To address this need, the team uses the Tree-Based Pipeline Optimization Tool (TPOT) to automatically identify the best ML pipelines for predicting cost and mass/energy flow outputs. This approach has been used with two promising bio-based jet fuel blendstocks: limonane and bisabolane. The results show that ML algorithms trained on simulation trials may serve as powerful surrogates for accurately approximating model outputs at a fraction of the computational expense.

 Led by Corinne Scown

The gpCAM project consists of an API and software designed to make autonomous data acquisition and analysis for experiments and simulations faster, simpler, and more widely available by leveraging active learning. The tool is based on a flexible and powerful Gaussian process regression at the core, which proves the ability to compute surrogate models and associated uncertainties. The flexibility and agnosticism stem from the modular design of gpCAM, which allows the user to implement and import their own Python functions to customize and control almost every aspect of the software. That makes it possible to easily tune the algorithm to account for various kinds of physics and other domain knowledge and constraints and to identify and find interesting features and function characteristics. A specialized function optimizer in gpCAM can take advantage of high performance computing (HPC) architectures for fast analysis time and reactive autonomous data acquisition.

 Led by Marcus Noack

Numerous DOE-relevant processes are becoming automated and adaptive using machine learning techniques. Such processes include vehicle and traffic navigation guidance, intelligent transportation systems, adaptive control of grid-attached equipment, and large scientific instruments. This creates a vulnerability for a cyber attacker to sabotage processes through tainted training data or specially crafted inputs. Consequences might be tainted manufactured output, traffic collisions, power outages, or damage to scientific instruments or experiments. This project is developing secure machine learning methods that will enable the safer operation of automated, adaptive, learning-driven “cyber-physical system” processes.

Led by Sean Peisert

The Supervisory Parameter Adjustment for Distribution Energy Storage (SPADES) project will develop methodology and tools allowing energy storage systems (ESS) to automatically reconfigure themselves to counteract cyberattacks against both the ESS control system directly and indirectly through the electric distribution grid. The reinforcement learning defensive algorithms will be integrated into the National Rural Electric Cooperative Association (NRECA) Open Modeling Framework (OMF), thereby allowing defensive strategies to be tailored on a utility-specific basis. The major outcomes of this project will be the tools to isolate the component of the ESS control system that has been compromised during a cyberattack, as well as policies for changing the control parameters of ESS to mitigate a wide variety of cyberattacks on both the ESS device itself and the electric distribution grid.

Led by Daniel Arnold

Berkeley Lab is leading two working groups relating to cybersecurity issues in inverter-based resources (IBR) and distributed energy resources (DER). The first working group is examining cybersecurity issues in AI-based automation for IBR/DER. Automation has brought significant advantages to the power grid for ensuring stability, increasing efficiency, and even providing cybersecurity benefits. At the same time, automation significantly increases cybersecurity risks because automated systems can be remotely attackable, and have similar vulnerabilities to other types of computing systems. The second working group is also working on data confidentiality issues for IBR/DER. Many data privacy and confidentiality issues arise when data is shared, but at the same time, data-sharing is essential to planning, research, and efficient operation. Understanding the intersection of confidentiality concerns and the role of privacy-preserving methods might enable both properties.

Led by Sean Peisert

Scientists are increasingly faced with ever more complex experiments. The vast dimensionality of parameter spaces underlying investigations in the biological, chemical, physical, and materials sciences challenges the most advanced data acquisition and analysis systems. While growing data-acquisition rates offer some relief, the complexity of experiments and the subtle dependence of the model function on input parameters remains daunting due to the sheer number of variables. 

This project aims to develop new stochastic process-based mathematical and computational methods to achieve high-quality, domain-aware function approximation, uncertainty quantification, and, by extension, autonomous experimentation. One product of this project is gpCAM, a simple-to-use, flexible, and HPC-ready Python-based software tool for Gaussian process-based function approximation and autonomous experimentation. 

Led by Marcus Noack

This project uses complex process simulation models for advanced biofuel and bioproduct production to develop and train machine learning (ML)-based surrogate models. Researchers need flexibility to explore different scenarios and understand how their work may impact upstream and downstream processes, as well as cost and greenhouse gas emissions. To address this need, the team uses the Tree-Based Pipeline Optimization Tool (TPOT) to automatically identify the best ML pipelines for predicting cost and mass/energy flow outputs. This  approach has been used with two promising bio-based jet fuel blendstocks: limonane and bisabolane. The results show that ML algorithms trained on simulation trials may serve as powerful surrogates for accurately approximating model outputs at a fraction of the computational expense. The web-based surrogate models are posted on lead.jbei.org. 

 Led by Corinne Scown.

With the growing penetration of wind, solar, and storage technologies, all interfaced to the grid via fast-acting power electronic converters (PECs), our power systems are rapidly evolving. One of the challenges with increased PECs is conducting computer time-series simulations of these systems, critical for understanding, and reliably operating, our electrical networks. The addition of PECs is resulting in these simulations taking significantly longer, under current approaches, due to their fast response rates, and spatial diversity.

This project aims to develop new tools at the intersection of scientific machine learning (SciML) and power systems engineering. These tools will accelerate the simulation of power systems with high penetration of PEC to ensure that we can simulate these systems in near real-time. This acceleration will be achieved by 1) using SciML to develop accurate models of aggregations of PECs in order to reduce the number of equations we need to solve and 2) using SciML to improve the mathematical techniques we use for solving these equations.

 Led by Duncan Callaway

As scientific datasets increase in both size and complexity, the ability to label, filter, and search this deluge of information has become a laborious, time-consuming, and sometimes impossible task without the help of automated tools. To overcome this challenge, researchers at Berkeley Lab are developing innovative machine learning tools to pull contextual information from scientific datasets and automatically generate metadata tags for each file. Scientists can then search these files via Science Search, a web-based search engine for scientific data. 
Led by Katie Antypas.

Experimental science is evolving. With the advent of new technology, scientific facilities are collecting data at increasing rates and higher resolution. But making sense of this data is becoming a major bottleneck. New mathematics and algorithms are needed to extract useful information from these experiments. To address these growing needs, the Center for Advanced Mathematics for Energy Research Applications (CAMERA) is working with scientists across disciplines to develop fundamental mathematics and algorithms, delivered as data analysis software that can accelerate scientific discovery. 

Led by James Sethian.

The overarching objective of this interdisciplinary science project is to leverage new theory and observations in land, atmosphere and space-based research to accurately partition global carbon fluxes between terrestrial ecosystems and the atmosphere at high spatial and temporal resolution. Machine learning, in particular simple and deep neural networks and generalized additive models, have proven powerful tools to do so. This project uses ML tools to both diagnose biases in global land surface models, and to derive new information from time-series of carbon fluxes between ecosystems and the atmosphere provided by distributed sensing networks such as AmeriFlux. Doing so both provides novel model diagnostics and amplifies the impact and utility of DOE investments in observational platforms. 

Led by Trevor Keenan.

Knowing how individuals move between places on the urban scale is critical to infrastructure and transportation systems planning. However, current route-choice models are stymied by the messy host of human factors that play into individual routing decisions. 

Using machine learning techniques and location data generated as a byproduct of smartphone application use, this project aims to better understand how individuals organize their travel plans into a set of routes and how similar behavior patterns emerge among distinct individual choices. This technique has the potential to inform demand management strategies that target individual users while generating large scale estimates that can be used in urban-wide traffic planning.

 Led by Marta C. Gonzalez.

Deep Learning and Satellite Imagery to Estimate Air Quality Impact at Scale, or DeepAir, uses deep learning algorithms to analyze satellite images combined with traffic information from cell phones and data already being collected by environmental sensors to improve air quality predictions. Scientists already use sophisticated models that consider factors such as wind speed, pressure, precipitation, and temperature to make predictions about pollution levels. DeepAir uses an array of distributed, existing datasources, including mobile phones, to help inventory man-made pollutants (such as vehicle exhaust and power plant emissions) as they actually enter the environment. 

The resulting analysis aims to ultimately inform the design of more efficient and more timely interventions, such as the San Francisco Bay Area's “Spare the Air” days.

Led by Marta C. González

Reconstructing the trajectories of charged particles from a collision event as they fly through a High Energy Physics (HEP) detector is a combinatorially difficult pattern recognition problem. Exa.TrkX is a collaboration of data scientists and computational physicists who are developing graph neural networks models aimed at reconstructing millions of particle trajectories per second from petabytes of raw data produced by the next generation of particle tracking detectors at the energy and intensity frontiers. Exa.TrkX is also exploring the scaling of distributed training of graph neural networks on U.S. Department of Energy pre-exascale systems and the deployment of graph neural network models with microsecond latencies on field-programmable gate array-based (FPGA-based) real-time processing systems.

Led by Paolo Calafiura.

In an effort to revolutionize agriculture and create sustainable farming practices that benefit both the environment and farms, researchers from Berkeley Lab, the University of Arkansas, and Glennoe Farms are bringing together molecular biology, biogeochemistry, environmental sensing technologies, and machine learning and applying them to soil research. This project aims to reduce the need for chemical fertilizers and enhance soil carbon uptake to improve the long-term viability of land and increase crop yields.  

Led by Ben Brown.

The high data-throughput of scientific instruments has made image recognition one of the most challenging problems in scientific research today. Supported by a U.S. DOE Early Career Award, Image across Domains, Experiments, Algorithms and Learning (IDEAL) focuses on computer vision and machine learning algorithms and software to enable timely interpretation of experimental data recorded as 2D or multispectral images. 

Led by Daniela Ushizima.

State of the art analytics software and modeling tools can provide valuable insights into efficiency opportunities. However, prior research has shown that key barriers include relatively limited data sources (smart meters and weather being most common in commercial tools), or reliance upon user-provided inputs for which default values may be the fallback. There is great opportunity to apply techniques based on multi-stream data fusion and machine learning to overcome these challenges. 

Led by Jessica Granderson.

As supercomputers become ever more capable in their march toward exascale levels of performance, scientists can run increasingly detailed and accurate simulations to study problems ranging from cleaner combustion to the nature of the universe. The challenge is that these powerful simulations are “computationally expensive,” consuming 10 to 50 million CPU hours for a single simulation. The ExaLearn project aims to develop new tools to help scientists overcome this challenge by applying machine learning to very large experimental datasets and simulations. 

Led by Peter Nugent.

The goal of this project is to improve bio-based product and fuel development through adaptive technoeconomic and performance modeling. Toward this end we are developing a comprehensive Feedstock to Function software tool (F2FT) that harnesses the power of machine learning to predict properties of high-potential molecules (fuels, fuel co-products, and other bioproducts) derived from biomass. This tool can also be used to evaluate the cost, benefits, and risk of promising biobased molecules or biofuels to enable faster, less expensive bioprocess optimization and scale-up. 

Led by Vi Rapp.

This project develops both simulation-based and simulation-independent deep learning techniques for high energy physics.  In particular, we are finding machine learning solutions to make the best use of physics-based simulations for inference.  Many of these physics-based simulations are too slow and and so we are also developing deep learning solutions to augment or supplant these simulations using generative models.

In parallel, we are developing simulation-independent techniques to broaden our analysis sensitivity.  These methods are less-than-supervised and include anomaly detection to search for new particles at the Large Hadron Collider. 

Led by Benjamin Nachman.

Population growth, changes in land use, climate change, and extreme weather are a few factors threatening not only freshwater supplies but all the other systems that rely on watersheds, including hydropower and agriculture.

To help meet these challenges, the DOE BER-funded ExaSheds project seeks to fundamentally change how watershed function is understood and predicted. Combining leadership class computers, big data, and machine learning with learning-assisted physics-based simulation tools, ExaSheds scientists are working to provide a full treatment of water flow and biogeochemical reactive transport at watershed to river basin scales.  

Led by Carl Steefel

The U.S. power distribution grid connects thousands of power plants to hundreds of millions of electricity customers across the country. In today's highly connected world, it is unrealistic to assume that energy delivery systems are isolated or immune from online threats; thus, as the grid is modernized, new features must be deployed to protect against cyberattacks. Berkeley Lab’s CIGAR project is developing and testing machine learning algorithms to counteract cyber-physical attacks that can compromise multiple systems in the electric grid.  Funded by the DOE Office of Cybersecurity, Energy Security, and Emergency Response (CESER)'s Cybersecurity for Energy Delivery Systems program. 

Led by Sean Peisert and Dan Arnold.