research
∙
04/08/2023
A multifidelity approach to continual learning for physical systems
We introduce a novel continual learning method based on multifidelity de...
share
research
∙
03/21/2023
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations
Physics-informed neural networks have emerged as an alternative method f...
research
∙
03/15/2023
A Multifidelity deep operator network approach to closure for multiscale systems
Projection-based reduced order models (PROMs) have shown promise in repr...
research
∙
03/15/2023
ViTO: Vision Transformer-Operator
We combine vision transformers with operator learning to solve diverse i...
research
∙
02/07/2023
SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics
We introduce adversarial learning methods for data-driven generative mod...
research
∙
01/26/2023
A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling
One of the most challenging and consequential problems in climate modeli...
research
∙
11/17/2022
SMS: Spiking Marching Scheme for Efficient Long Time Integration of Differential Equations
We propose a Spiking Neural Network (SNN)-based explicit numerical schem...
research
∙
04/19/2022
Multifidelity Deep Operator Networks
Operator learning for complex nonlinear operators is increasingly common...
research
∙
03/03/2022
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery
Numerical modeling and simulation have become indispensable tools for ad...
research
∙
11/09/2021
Machine-learning custom-made basis functions for partial differential equations
Spectral methods are an important part of scientific computing's arsenal...
research
∙
09/11/2021
Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling
Discovery of dynamical systems from data forms the foundation for data-d...
research
∙
06/23/2021
Machine learning structure preserving brackets for forecasting irreversible processes
Forecasting of time-series data requires imposition of inductive biases ...
research
∙
06/21/2021
Physics-constrained deep neural network method for estimating parameters in a redox flow battery
In this paper, we present a physics-constrained deep neural network (PCD...
research
∙
03/04/2021
Time-dependent stochastic basis adaptation for uncertainty quantification
We extend stochastic basis adaptation and spatial domain decomposition m...
research
∙
01/24/2021
Optimal renormalization of multi-scale systems
While model order reduction is a promising approach in dealing with mult...
research
∙
12/19/2019
Model reduction for a power grid model
We apply model reduction techniques to the DeMarco power grid model. The...
research
∙
12/16/2019
A Kinetic Monte Carlo Approach for Simulating Cascading Transmission Line Failure
In this work, cascading transmission line failures are studied through a...
research
∙
05/17/2019
Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning
We assume that we are given a time series of data from a dynamical syste...
research
∙
04/02/2019
A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations
We investigate the use of discrete and continuous versions of physics-in...
research
∙
05/13/2018
Doing the impossible: Why neural networks can be trained at all
As deep neural networks grow in size, from thousands to millions to bill...
research
∙
03/22/2018
