11 citations · 13 across the 3 of their papers we have counts for
3 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…
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…
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…