1 code implementation • 16 Oct 2024 • Qidong Yang, Jonathan Giezendanner, Daniel Salles Civitarese, Johannes Jakubik, Eric Schmitt, Anirban Chandra, Jeremy Vila, Detlef Hohl, Chris Hill, Campbell Watson, Sherrie Wang
In this work, we train a heterogeneous graph neural network (GNN) end-to-end to downscale gridded forecasts to off-grid locations of interest.
no code implementations • 11 Oct 2022 • Rishikesh Ranade, Chris Hill, Lalit Ghule, Jay Pathak
The numerical experiments show that our approach outperforms ML baselines in terms of 1) accuracy across quantitative metrics and 2) generalization to out-of-distribution conditions as well as domain sizes.
no code implementations • 7 Oct 2021 • Rishikesh Ranade, Chris Hill, Haiyang He, Amir Maleki, Norman Chang, Jay Pathak
Numerical simulations for engineering applications solve partial differential equations (PDE) to model various physical processes.
no code implementations • 6 Apr 2021 • Rishikesh Ranade, Chris Hill, Haiyang He, Amir Maleki, Jay Pathak
In this work we propose a hybrid solver to solve partial differential equation (PDE)s in the latent space.
no code implementations • 2 Mar 2021 • Jeffrey C. Carver, Ian A. Cosden, Chris Hill, Sandra Gesing, Daniel S. Katz
Research software is a class of software developed to support research.
Software Engineering
no code implementations • 17 May 2020 • Rishikesh Ranade, Chris Hill, Jay Pathak
The two solver characteristics that have been adopted in this work are: 1) the use of discretization-based schemes to approximate spatio-temporal partial derivatives and 2) the use of iterative algorithms to solve linearized PDEs in their discrete form.