5 papers
Probabilistic Retrofitting of Learned Simulators
Cristiana Diaconu, Miles Cranmer, Richard E. Turner +2
Dominant approaches for modelling Partial Differential Equations (PDEs) rely on deterministic predictions, yet many physical systems of interest are inherently chaotic and uncertai…
Pre-Generating Multi-Difficulty PDE Data for Few-Shot Neural PDE Solvers
Naman Choudhary, Vedant Singh, Ameet Talwalkar +3
A key aspect of learned partial differential equation (PDE) solvers is that the main cost often comes from generating training data with classical solvers rather than learning the…
Chimera: State Space Models Beyond Sequences
Aakash Lahoti, Tanya Marwah, Ratish Puduppully +1
Transformer-based deep learning methods have become the standard approach for modeling diverse data such as sequences, images, and graphs. These methods rely on self-attention, whi…
Adapting Language Models via Token Translation
Zhili Feng, Tanya Marwah, Nicolo Fusi +2
Modern large language models use a fixed tokenizer to effectively compress text drawn from a source domain. However, applying the same tokenizer to a new target domain often leads…
UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation
Junhong Shen, Tanya Marwah, Ameet Talwalkar
We present Unified PDE Solvers (UPS), a data- and compute-efficient approach to developing unified neural operators for diverse families of spatiotemporal PDEs from various domains…