Scientific Machine Learning Comparison Hub
4 papers - avg viability 4.5
Top Papers
- Two Teachers Better Than One: Hardware-Physics Co-Guided Distributed Scientific Machine Learning(7.0)
EPIC is a distributed SciML framework that reduces communication costs while preserving physical fidelity for real-time applications.
- ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning(6.0)
Build a geometry-aware transformer for scalable surrogate modeling in scientific machine learning.
- Physics-informed fine-tuning of foundation models for partial differential equations(4.0)
A physics-informed fine-tuning framework for adapting foundation models to partial differential equations with minimal data.
- Learning Where the Physics Is: Probabilistic Adaptive Sampling for Stiff PDEs(1.0)
A probabilistic framework improving the efficiency of Physics-Informed Extreme Learning Machines for modeling stiff PDEs.