Mar 11, 2024 · We present Unified PDE Solvers (UPS), a data- and compute-efficient approach to developing unified neural operators for diverse families of spatiotemporal PDEs.
Mar 11, 2024 · We introduce UPS (Unified PDE Solver), an effective and data-efficient approach to solve diverse spatiotemporal PDEs defined over various ...
Original PyTorch implementation of UPS proposed in the paper "UPS: Towards Building Foundation Models for PDE Solving via Cross-Modal Adaptation".
We present Unified PDE Solvers (UPS), a data- and compute-efficient approach to developing unified neural operators for diverse families of spatiotemporal ...
Aug 9, 2024 · The cross-modal UPS achieves state-of-the-art results on a wide range of 1D and 2D PDE families from PDEBench, outperforming existing unified ...
Jul 31, 2024 · The key idea behind UPS is to leverage the cross-modal adaptation capabilities of large language models to solve a diverse range of PDEs. The ...
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Aug 20, 2024 · We present Unified PDE Solvers (UPS), a data- and compute-efficient approach to developing unified neural operators for diverse families of ...
Aug 31, 2024 · The cross-modal UPS achieves state-of-the-art results on a wide range of 1D and 2D PDE families from PDEBench, outperforming existing unified ...
We present Unified PDE Solvers (UPS), a data- and compute-efficient approach to developing unified neural operators for diverse families of spatiotemporal PDEs ...
Mar 17, 2024 · It employs a two-stage cross-modal adaptation process, incorporating modality alignment and multi-task learning, which enables strong empirical ...