ResearchTrend.AI
Functional Tensor Decompositions for Physics-Informed Neural Networks

Functional Tensor Decompositions for Physics-Informed Neural Networks

Sai Karthikeya Vemuri
Tim Buchner
Julia Niebling
Joachim Denzler
    PINN
ArXivPDFHTML

Papers citing "Functional Tensor Decompositions for Physics-Informed Neural Networks"

4 / 4 papers shown
Title
Respecting causality is all you need for training physics-informed neural networks
Respecting causality is all you need for training physics-informed neural networks
Sizhuang He
Shyam Sankaran
P. Perdikaris
PINN
CML
AI4CE
128
200
0
14 Mar 2022
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
L. McClenny
U. Braga-Neto
PINN
65
452
0
07 Sep 2020
When and why PINNs fail to train: A neural tangent kernel perspective
When and why PINNs fail to train: A neural tangent kernel perspective
Sizhuang He
Xinling Yu
P. Perdikaris
101
896
0
28 Jul 2020
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
868
149,474
0
22 Dec 2014

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.