Profitability of Merchant Investments in Battery Energy Storage Systems: Methods and Case Studies
Abstract not provided.
Publications
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Jump to search filtersParallel-In-Time Training of Recurrent Neural Networks
Abstract not provided.
Evaluating Demand Response (DR) Opportunities for Power Systems Resilience Using MILP and MINLP Formulations
Abstract not provided.
Self-Induced Curvature in an Internally Loaded Peridynamic Fiber
A straight fiber with nonlocal forces that are independent of bond strain is considered. These internal loads can either stabilize or destabilize the straight configuration. Transverse waves with long wavelength have unstable dispersion properties for certain combinations of nonlocal kernels and internal loads. When these unstable waves occur, deformation of the straight fiber into a circular arc can lower its potential energy in equilibrium. The equilibrium value of the radius of curvature is computed explicitly.
Comparing Instrumentation Selection Techniques for Vibration Testing
Abstract not provided.
An Augmented Lagrangian Approach for Risk-Averse PDE-Constrained Optimization with State Constraints
Abstract not provided.
Trilinos for Exascale
Abstract not provided.
Reinforcement Learning for PDE Control Problems
Abstract not provided.
Improving Digital Twins by Learning from a Fleet of Assets
Abstract not provided.
Error-in-variables modelling for operator learning
Abstract not provided.
Interface Flux Recovery Framework for Constructing Partitioned Heterogeneous Time-Integration Methods
Abstract not provided.
Error-in-variables modelling for operator learning
Abstract not provided.
Fugu and N2A Lava Support
Abstract not provided.
Use of a machine learning model for a constitutive chemistry model within a groundwater flow and transport application modeling nuclear fuel degradation in a waste repository
Abstract not provided.
A Matrix-Free Approach for Algebraic Multigrid
Abstract not provided.
Drift Matters, Improving Unsupervised Anomaly Detection in National Security Applications
Abstract not provided.
Quantification and Propagation of Uncertainties in Machine Learning Interatomic Potentials for Molecular Dynamics
Abstract not provided.
Physics-constrained Bayesian inference of an uncertain operator in the sparse-data regime
Abstract not provided.
COINFLIPS: CO-designed Improved Neural Foundations Leveraging Inherent Physics Stochasticity
Abstract not provided.
Quantum control in the era of quantum computing
Abstract not provided.
Meshless Remap of Native Fields for Earth System Models via Generalized Moving Least Squares
Abstract not provided.
Using Neuromorphic Computing Methods for General Computer Performance Growth
Abstract not provided.
Inverse Methods - Users Manual 5.6
The inverse methods team provides a set of tools for solving inverse problems in structural dynamics and thermal physics, and also sensor placement optimization via Optimal Experimental Design (OED). These methods are used for designing experiments, model calibration, and verfication/validation analysis of weapons systems. This document provides a user's guide to the input for the three apps that are supported for these methods. Details of input specifications, output options, and optimization parameters are included.
Enabling and interpreting hyper-differential sensitivity analysis for Bayesian inverse problems
Abstract not provided.
An Accurate, Error-Tolerant, and Energy-Efficient Neural Network Inference Engine Based on SONOS Analog Memory
IEEE Transactions on Circuits and Systems I: Regular Papers
We demonstrate SONOS (silicon-oxide-nitride-oxide-silicon) analog memory arrays that are optimized for neural network inference. The devices are fabricated in a 40nm process and operated in the subthreshold regime for in-memory matrix multiplication. Subthreshold operation enables low conductances to be implemented with low error, which matches the typical weight distribution of neural networks, which is heavily skewed toward near-zero values. This leads to high accuracy in the presence of programming errors and process variations. We simulate the end-To-end neural network inference accuracy, accounting for the measured programming error, read noise, and retention loss in a fabricated SONOS array. Evaluated on the ImageNet dataset using ResNet50, the accuracy using a SONOS system is within 2.16% of floating-point accuracy without any retraining. The unique error properties and high On/Off ratio of the SONOS device allow scaling to large arrays without bit slicing, and enable an inference architecture that achieves 20 TOPS/W on ResNet50, a > 10× gain in energy efficiency over state-of-The-Art digital and analog inference accelerators.
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