A neural network dynamics emulator trained on data yields stability eigenmodes and resolvent modes via automatic differentiation of its Jacobian, enabling equation-free analysis of nonlinear systems.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Neural actor-critic method for high-dimensional HJB PDEs converges in Sobolev space to an infinite-dimensional ODE whose fixed points solve the stochastic control problem under a convexity-like Hamiltonian assumption, with numerical success up to 200 dimensions.
citing papers explorer
-
A neural operator framework for data-driven discovery of stability and receptivity in physical systems
A neural network dynamics emulator trained on data yields stability eigenmodes and resolvent modes via automatic differentiation of its Jacobian, enabling equation-free analysis of nonlinear systems.
-
Neural Actor-Critic Methods for Hamilton-Jacobi-Bellman PDEs: Asymptotic Analysis and Numerical Studies
Neural actor-critic method for high-dimensional HJB PDEs converges in Sobolev space to an infinite-dimensional ODE whose fixed points solve the stochastic control problem under a convexity-like Hamiltonian assumption, with numerical success up to 200 dimensions.