Differentiable simulation enables gradients to be back-propagated throug...
Function approximation has been an indispensable component in modern
rei...
We consider the inverse acoustic obstacle problem for sound-soft star-sh...
Game theory has been an effective tool in the control of disease spread ...
In recent years, an increasing amount of work has focused on differentia...
One of the core problems in mean-field control and mean-field games is t...
We propose an efficient, reliable, and interpretable global solution met...
Partial differential equations (PDEs) play a dominant role in the
mathem...
Most existing theoretical analysis of reinforcement learning (RL) is lim...
This paper concerns the convergence of empirical measures in high dimens...
Reinforcement learning (RL) algorithms based on high-dimensional functio...
Constitutive models are widely used for modelling complex systems in sci...
We propose a novel numerical method for high dimensional
Hamilton–Jacobi...
Stochastic control problems with delay are challenging due to the
path-d...
Game theory has been an effective tool in the control of disease spread ...
We study the approximation properties and optimization dynamics of recur...
In recent years, tremendous progress has been made on numerical algorith...
Reinforcement learning is a powerful tool to learn the optimal policy of...
Stochastic differential games have been used extensively to model agents...
Machine learning is poised as a very powerful tool that can drastically
...
This paper develops further the idea of perturbed gradient descent, by
a...
We propose a new method to solve eigenvalue problems for linear and
semi...
We propose a deep neural network-based algorithm to identify the Markovi...
We consider universal approximations of symmetric and anti-symmetric
fun...
The recently proposed numerical algorithm, deep BSDE method, has shown
r...
Recent work linking deep neural networks and dynamical systems opened up...
Recent developments in many-body potential energy representation via dee...
We propose a new algorithm for solving parabolic partial differential
eq...
Many real world stochastic control problems suffer from the "curse of
di...