6 papers
Spectral Signatures of Large Language Models
Zhuoying Zhang, Ishan V. Prasad, Yuanzhe Hu +4
The rapidly growing repository of publicly available large language models (LLMs) presents significant challenges for systematic management and quantification at scale, such as mod…
Learning, Solving and Optimizing PDEs with TensorGalerkin: an efficient high-performance Galerkin assembly algorithm
Shizheng Wen, Mingyuan Chi, Tianwei Yu +5
We present a unified algorithmic framework for the numerical solution, constrained optimization, and physics-informed learning of PDEs with a variational structure. Our framework i…
RELift: Learned Coarse-to-Fine Propagators for Time-Dependent PDEs with Applications to Electron Dynamics
Hardeep Bassi, Yuanran Zhu, Erika Ye +5
We present RELift (Restrict, Evolve, Lift), a two-phase learning framework that couples coarse-grid numerical solvers with neural operators to super-resolve and forecast fine-grid…
SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning
Pu Ren, N. Benjamin Erichson, Junyi Guo +4
Super-resolution (SR) techniques aim to enhance data resolution, enabling the retrieval of finer details, and improving the overall quality and fidelity of the data representation.…
Reasoning-Enhanced Object-Centric Learning for Videos
Jian Li, Pu Ren, Yang Liu +1
Object-centric learning aims to break down complex visual scenes into more manageable object representations, enhancing the understanding and reasoning abilities of machine learnin…
Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction
Yuan Mi, Pu Ren, Hongteng Xu +6
Data-centric methods have shown great potential in understanding and predicting spatiotemporal dynamics, enabling better design and control of the object system. However, deep lear…