Search Results for author:
Found 14 papers, 2 papers with code
Learning WENO for entropy stable schemes to solve conservation laws
In the present work, we propose a variant of the SP-WENO, termed as Deep Sign-Preserving WENO (DSP-WENO), where a neural network is trained to learn the WENO weighting strategy.
Generative Algorithms for Fusion of Physics-Based Wildfire Spread Models with Satellite Data for Initializing Wildfire Forecasts
The cWGAN is used to produce samples of likely fire arrival times from the conditional distribution of arrival times given satellite active fire detections.
Learning end-to-end inversion of circular Radon transforms in the partial radial setup
We present a deep learning-based computational algorithm for inversion of circular Radon transforms in the partial radial setup, arising in photoacoustic tomography.
Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty
The cWGAN developed in this work differs from earlier versions in that its critic is required to be 1-Lipschitz with respect to both the inferred and the measurement vectors and not just the former.
Deep Learning and Computational Physics (Lecture Notes)
These notes were compiled as lecture notes for a course developed and taught at the University of the Southern California.
Variationally Mimetic Operator Networks
The application of the VarMiON to a canonical elliptic PDE and a nonlinear PDE reveals that for approximately the same number of network parameters, on average the VarMiON incurs smaller errors than a standard DeepONet and a recently proposed multiple-input operator network (MIONet).
The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems
In this work, we train conditional Wasserstein generative adversarial networks to effectively sample from the posterior of physics-based Bayesian inference problems.
Efficient posterior inference & generalization in physics-based Bayesian inference with conditional GANs
In this work, we propose a conditional generative adversarial network (cGAN) to sample from the posterior of physics-based Bayesian inference problems.
Solution of Physics-based Bayesian Inverse Problems with Deep Generative Priors
Specifically, we demonstrate how using the approximate distribution learned by a Generative Adversarial Network (GAN) as a prior in a Bayesian update and reformulating the resulting inference problem in the low-dimensional latent space of the GAN, enables the efficient solution of large-scale Bayesian inverse problems.
Bayesian Inference in Physics-Driven Problems with Adversarial Priors
Generative adversarial networks (GANs) have found multiple applications in the solution of inverse problems in science and engineering.
Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks
We present a novel active learning algorithm, termed as iterative surrogate model optimization (ISMO), for robust and efficient numerical approximation of PDE constrained optimization problems.
On the approximation of rough functions with deep neural networks
Deep neural networks and the ENO procedure are both efficient frameworks for approximating rough functions.
Constraint-Aware Neural Networks for Riemann Problems
Neural networks are increasingly used in complex (data-driven) simulations as surrogates or for accelerating the computation of classical surrogates.
Deep learning observables in computational fluid dynamics
Under the assumption that the underlying neural networks generalize well, we prove that the deep learning MC and QMC algorithms are guaranteed to be faster than the baseline (quasi-) Monte Carlo methods.
