Physics-informed machine learning (PIML) is a set of methods and tools t...
Physics-informed neural networks (PINNs) provide a framework to build
su...
Differentiable simulation enables gradients to be back-propagated throug...
Transformer layers, which use an alternating pattern of multi-head atten...
Emergency vehicles (EMVs) play a crucial role in responding to time-crit...
This work presents a physics-informed neural network based framework to ...
Emergency vehicles (EMVs) play a crucial role in responding to time-crit...
The incorporation of appropriate inductive bias plays a critical role in...
The last few years have witnessed an increased interest in incorporating...
Model-free reinforcement learning (RL), in particular Q-learning is wide...
Incompressible fluid flow around a cylinder is one of the classical prob...
In this work, we introduce Dissipative SymODEN, a deep learning architec...
Motivated by real-world applications of unmanned aerial vehicles, this p...
Microstructures of a material form the bridge linking processing conditi...
In this paper, we introduce Symplectic ODE-Net (SymODEN), a deep learnin...