7 citations · 20 across the 9 of their papers we have counts for
9 papers
Approximation Theory and Applications of Randomized Neural Networks for Solving High-Dimensional PDEs
T. De Ryck, S. Mishra, Y. Shang +1
We present approximation results and numerical experiments for the use of randomized neural networks within physics-informed extreme learning machines to efficiently solve high-dim…
Overlapping Schwarz Preconditioners for Randomized Neural Networks with Domain Decomposition
Yong Shang, Alexander Heinlein, Siddhartha Mishra +1
Randomized neural networks (RaNNs), in which hidden layers remain fixed after random initialization, provide an efficient alternative for parameter optimization compared to fully p…
Phase-Field Modeling of Fracture with Physics-Informed Deep Learning
M. Manav, R. Molinaro, S. Mishra +1
We explore the potential of the deep Ritz method to learn complex fracture processes such as quasistatic crack nucleation, propagation, kinking, branching, and coalescence within t…
Efficient Computation of Large-Scale Statistical Solutions to Incompressible Fluid Flows
Tobias Rohner, Siddhartha Mishra
This work presents the development, performance analysis and subsequent optimization of a GPU-based spectral hyperviscosity solver for turbulent flows described by the three dimens…
Neural Oscillators are Universal
Samuel Lanthaler, T. Konstantin Rusch, Siddhartha Mishra
Coupled oscillators are being increasingly used as the basis of machine learning (ML) architectures, for instance in sequence modeling, graph representation learning and in physica…
A Monte-Carlo ab-initio algorithm for the multiscale simulation of compressible multiphase flows
Marco Petrella, Remi Abgrall, Siddhartha Mishra
We propose a novel Monte-Carlo based ab-initio algorithm for directly computing the statistics for quantities of interest in an immiscible two-phase compressible flow. Our algorith…