126 citations · 220 across the 6 of their papers we have counts for
6 papers
Using Physics Informed Neural Networks for Supernova Radiative Transfer Simulation
Xingzhuo Chen, David J. Jeffery, Ming Zhong +3
We use physics informed neural networks (PINNs) to solve the radiative transfer equation and calculate a synthetic spectrum for a Type Ia supernova (SN~Ia) SN 2011fe. The calculati…
Physics-Informed Neural Networks with Adaptive Localized Artificial Viscosity
E. J. R. Coutinho, M. Dall'Aqua, L. McClenny +3
Physics-informed Neural Network (PINN) is a promising tool that has been applied in a variety of physical phenomena described by partial differential equations (PDE). However, it h…
Experimentally Validated Multiphysics Modeling of Fracture Induced by Thermal Shocks in Sintered UO2 Pellets
Levi D. McClenny, Moiz I. Butt, M. Gomaa Abdoelatef +8
Commercial nuclear power plants extensively rely on fission energy from uranium dioxide (UO2) fuel pellets that provide thermal energy; consequently, generating carbon-free power i…
TensorDiffEq: Scalable Multi-GPU Forward and Inverse Solvers for Physics Informed Neural Networks
Levi D. McClenny, Mulugeta A. Haile, Ulisses M. Braga-Neto
Physics-Informed Neural Networks promise to revolutionize science and engineering practice, by introducing domain-aware deep machine learning models into scientific computation. Se…
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism
Levi McClenny, Ulisses Braga-Neto
Physics-Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equati…
Deep Multimodal Transfer-Learned Regression in Data-Poor Domains
Levi McClenny, Mulugeta Haile, Vahid Attari +3
In many real-world applications of deep learning, estimation of a target may rely on various types of input data modes, such as audio-video, image-text, etc. This task can be furth…