1 citations · 1 across the 3 of their papers we have counts for
5 papers
Phaedra: Learning High-Fidelity Discrete Tokenization for the Physical Science
Levi Lingsch, Georgios Kissas, Johannes Jakubik +1
Tokens are discrete representations that allow modern deep learning to scale by transforming high-dimensional data into sequences that can be efficiently learned, generated, and ge…
Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary Domains
Shizheng Wen, Arsh Kumbhat, Levi Lingsch +4
The very challenging task of learning solution operators of PDEs on arbitrary domains accurately and efficiently is of vital importance to engineering and industrial simulations. D…
Neuro-Symbolic AI for Analytical Solutions of Differential Equations
Orestis Oikonomou, Levi Lingsch, Dana Grund +2
Analytical solutions to differential equations offer exact, interpretable insight but are rarely available because discovering them requires expert intuition or exhaustive search o…
RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains
Sepehr Mousavi, Shizheng Wen, Levi Lingsch +3
Learning the solution operators of PDEs on arbitrary domains is challenging due to the diversity of possible domain shapes, in addition to the often intricate underlying physics. W…
FUSE: Fast Unified Simulation and Estimation for PDEs
Levi E. Lingsch, Dana Grund, Siddhartha Mishra +1
The joint prediction of continuous fields and statistical estimation of the underlying discrete parameters is a common problem for many physical systems, governed by PDEs. Hitherto…