Physics informed WNO
Deep neural operators are recognized as an effective tool for learning solution operators of
complex partial differential equations (PDEs). As compared to laborious analytical and …
complex partial differential equations (PDEs). As compared to laborious analytical and …
Probabilistic machine learning based predictive and interpretable digital twin for dynamical systems
A framework for creating and updating digital twins for dynamical systems from a library of
physics-based functions is proposed. The sparse Bayesian machine learning is used to …
physics-based functions is proposed. The sparse Bayesian machine learning is used to …
Waveformer for modeling dynamical systems
N Navaneeth, S Chakraborty - Mechanical Systems and Signal Processing, 2024 - Elsevier
Neural operators have gained recognition as potent tools for learning solutions of a family of
partial differential equations. The state-of-the-art neural operators excel at approximating the …
partial differential equations. The state-of-the-art neural operators excel at approximating the …
On the locality of local neural operator in learning fluid dynamics
X Ye, H Li, J Huang, G Qin - Computer Methods in Applied Mechanics and …, 2024 - Elsevier
This paper launches a thorough discussion on the locality of local neural operator (LNO),
which is the core that enables LNO great flexibility on varied computational domains in …
which is the core that enables LNO great flexibility on varied computational domains in …
DPA-WNO: A gray box model for a class of stochastic mechanics problem
S Chakraborty - arXiv preprint arXiv:2309.15128, 2023 - arxiv.org
The well-known governing physics in science and engineering is often based on certain
assumptions and approximations. Therefore, analyses and designs carried out based on …
assumptions and approximations. Therefore, analyses and designs carried out based on …
PhyPlan: Compositional and Adaptive Physical Task Reasoning with Physics-Informed Skill Networks for Robot Manipulators
H Vagadia, M Chopra, A Barnawal, T Banerjee… - arXiv preprint arXiv …, 2024 - arxiv.org
Given the task of positioning a ball-like object to a goal region beyond direct reach, humans
can often throw, slide, or rebound objects against the wall to attain the goal. However …
can often throw, slide, or rebound objects against the wall to attain the goal. However …
Physics informed WNO
T Tripura, S Chakraborty - arXiv preprint arXiv:2302.05925, 2023 - arxiv.org
Deep neural operators are recognized as an effective tool for learning solution operators of
complex partial differential equations (PDEs). As compared to laborious analytical and …
complex partial differential equations (PDEs). As compared to laborious analytical and …
A physics-informed transformer neural operator for learning generalized solutions of initial boundary value problems
SK Boya, D Subramani - arXiv preprint arXiv:2412.09009, 2024 - arxiv.org
Initial boundary value problems arise commonly in applications with engineering and
natural systems governed by nonlinear partial differential equations (PDEs). Operator …
natural systems governed by nonlinear partial differential equations (PDEs). Operator …
Harnessing physics-informed operators for high-dimensional reliability analysis problems
N Navaneeth, S Chakraborty - arXiv preprint arXiv:2409.04708, 2024 - arxiv.org
Reliability analysis is a formidable task, particularly in systems with a large number of
stochastic parameters. Conventional methods for quantifying reliability often rely on …
stochastic parameters. Conventional methods for quantifying reliability often rely on …
PhyPlan: Generalizable and Rapid Physical Task Planning with Physics Informed Skill Networks for Robot Manipulators
M Chopra, A Barnawal, H Vagadia, T Banerjee… - arXiv preprint arXiv …, 2024 - arxiv.org
Given the task of positioning a ball-like object to a goal region beyond direct reach, humans
can often throw, slide, or rebound objects against the wall to attain the goal. However …
can often throw, slide, or rebound objects against the wall to attain the goal. However …