JMLRSep 17, 2024

'From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs', by Lorenz Richter, Leon Sallandt, Nikolas Nüsken.

http://jmlr.org/papers/v25/23-0982.html

#discretization #tensor #numerically

From continuous-time formulations to discretization schemes: tensor trains and robust regression for BSDEs and parabolic PDEs

💧🌏 Greg CocksMay 14, 2024
Watershed Delineation On A Hexagonal Mesh Grid
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https://doi.org/10.1016/j.envsoft.2020.104702 <-- shared paper
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#GIS #spatial #mapping #model #modeling #Hydrology #Hexagon #WatershedDelineation #DigitalGlobalGridSystem #DGS #watershed #water #delineation #scaling #discretization #discretisation #simulations #numerical #adjacency #d8 #HexWatershed #watersheds #catchments #drainageareas
New Submissions to TMLRJul 17, 2023

Deep Operator Learning Lessens the Curse of Dimensionality for PDEs

https://openreview.net/forum?id=zmBFzuT2DN

#pdes #pde #discretization

Deep Operator Learning Lessens the Curse of Dimensionality for PDEs

Deep neural networks (DNNs) have achieved remarkable success in numerous domains, and their application to PDE-related problems has been rapidly advancing. This paper provides an estimate for the...

OpenReview
JMLRApr 28, 2023

'Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs', by Nikola Kovachki et al.

http://jmlr.org/papers/v24/21-1524.html

#discretization #operators #pdes

Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs