3 citations · 3 across the 8 of their papers we have counts for
4 papers · 1 filter
ReBaNO: Reduced Basis Neural Operator Mitigating Generalization Gaps and Achieving Discretization Invariance
Haolan Zheng, Yanlai Chen, Jiequn Han +1
We propose a novel data-lean operator learning algorithm, the Reduced Basis Neural Operator (ReBaNO), to solve a group of PDEs with multiple distinct inputs. Inspired by the Reduce…
An Attention-based Spatio-Temporal Neural Operator for Evolving Physics
Vispi Karkaria, Doksoo Lee, Yi-Ping Chen +2
In scientific machine learning (SciML), a key challenge is learning unknown, evolving physical processes and making predictions across spatio-temporal scales. For example, in real-…
Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery
Ning Liu, Yue Yu
Attention mechanisms have emerged as transformative tools in core AI domains such as natural language processing and computer vision. Yet, their largely untapped potential for mode…
Large language models, physics-based modeling, experimental measurements: the trinity of data-scarce learning of polymer properties
Ning Liu, Siavash Jafarzadeh, Brian Y. Lattimer +3
Large language models (LLMs) bear promise as a fast and accurate material modeling paradigm for evaluation, analysis, and design. Their vast number of trainable parameters necessit…