no code implementations • 24 Jun 2023 • Truong X. Nghiem, Ján Drgoňa, Colin Jones, Zoltan Nagy, Roland Schwan, Biswadip Dey, Ankush Chakrabarty, Stefano Di Cairano, Joel A. Paulson, Andrea Carron, Melanie N. Zeilinger, Wenceslao Shaw Cortez, Draguna L. Vrabie
Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of PIML models; and 4) physics-informed digital twins.
no code implementations • 9 Jun 2023 • Shinjan Ghosh, Amit Chakraborty, Georgia Olympia Brikis, Biswadip Dey
Physics-informed neural networks (PINNs) provide a framework to build surrogate models for dynamical systems governed by differential equations.
1 code implementation • 28 Apr 2023 • Yaofeng Desmond Zhong, Jiequn Han, Biswadip Dey, Georgia Olympia Brikis
We find that existing differentiable simulation methods provide inaccurate gradients when the contact normal direction is not fixed - a general situation when the contacts are between two moving objects.
no code implementations • 12 Dec 2022 • Yaofeng Desmond Zhong, Tongtao Zhang, Amit Chakraborty, Biswadip Dey
Our experiments show that this simple modification improves the performance of transformer networks in multiple tasks.
no code implementations • 7 Aug 2022 • Udit Halder, Vidya Raju, Matteo Mischiati, Biswadip Dey, P. S. Krishnaprasad
These behaviors may be viewed as flock-scale strategies, emerging from interactions between individuals, accomplishing some collective adaptive purpose such as finding a roost, or mitigating the danger from predator attacks.
no code implementations • 27 Jun 2022 • Haoran Su, Yaofeng D. Zhong, Joseph Y. J. Chow, Biswadip Dey, Li Jin
Emergency vehicles (EMVs) play a crucial role in responding to time-critical calls such as medical emergencies and fire outbreaks in urban areas.
Multi-agent Reinforcement Learning Reinforcement Learning (RL)
no code implementations • 5 May 2022 • Tongtao Zhang, Biswadip Dey, Krishna Veeraraghavan, Harshad Kulkarni, Amit Chakraborty
Computational fluid dynamics (CFD) simulations, a critical tool in various engineering applications, often require significant time and compute power to predict flow properties.
no code implementations • 20 Jan 2022 • Rajat Arora, Pratik Kakkar, Biswadip Dey, Amit Chakraborty
This work presents a physics-informed neural network (PINN) based framework to model the strain-rate and temperature dependence of the deformation fields in elastic-viscoplastic solids.
no code implementations • 30 Oct 2021 • Haoran Su, Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
Consequently, the decentralized RL agents learn network-level cooperative traffic signal phase strategies that reduce EMV travel time and the average travel time of non-EMVs in the network.
no code implementations • 12 Sep 2021 • Haoran Su, Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
EMVLight extends Dijkstra's algorithm to efficiently update the optimal route for the EMVs in real time as it travels through the traffic network.
1 code implementation • NeurIPS 2021 • Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
In this paper, we introduce a differentiable contact model, which can capture contact mechanics: frictionless/frictional, as well as elastic/inelastic.
no code implementations • 3 Dec 2020 • Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
The last few years have witnessed an increased interest in incorporating physics-informed inductive bias in deep learning frameworks.
no code implementations • 11 Nov 2020 • Udari Madhushani, Biswadip Dey, Naomi Ehrich Leonard, Amit Chakraborty
Value function based reinforcement learning (RL) algorithms, for example, $Q$-learning, learn optimal policies from datasets of actions, rewards, and state transitions.
no code implementations • 3 Nov 2020 • Tongtao Zhang, Biswadip Dey, Pratik Kakkar, Arindam Dasgupta, Amit Chakraborty
We demonstrate this approach by predicting simulation results over out of range time interval and for novel design conditions.
1 code implementation • ICLR Workshop DeepDiffEq 2019 • Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
In this work, we introduce Dissipative SymODEN, a deep learning architecture which can infer the dynamics of a physical system with dissipation from observed state trajectories.
no code implementations • 4 Oct 2019 • Akshay Iyer, Biswadip Dey, Arindam Dasgupta, Wei Chen, Amit Chakraborty
Microstructures of a material form the bridge linking processing conditions - which can be controlled, to the material property - which is the primary interest in engineering applications.
1 code implementation • ICLR 2020 • Yaofeng Desmond Zhong, Biswadip Dey, Amit Chakraborty
In this paper, we introduce Symplectic ODE-Net (SymODEN), a deep learning framework which can infer the dynamics of a physical system, given by an ordinary differential equation (ODE), from observed state trajectories.
no code implementations • NeurIPS 2017 • Noga Alon, Daniel Reichman, Igor Shinkar, Tal Wagner, Sebastian Musslick, Jonathan D. Cohen, Tom Griffiths, Biswadip Dey, Kayhan Ozcimder
A key feature of neural network architectures is their ability to support the simultaneous interaction among large numbers of units in the learning and processing of representations.