6 papers
Unveiling Multi-regime Patterns in SciML: Distinct Failure Modes and Regime-specific Optimization
Yuxin Wang, Yuanzhe Hu, Xiaokun Zhong +7
Neural networks trained under different hyperparameter settings can fall into distinct training "regimes," with consistent behavior within regimes and qualitative differences acros…
Iterative Refinement Neural Operators are Learned Fixed-Point Solvers: A Principled Approach to Spectral Bias Mitigation
Xiaotian Liu, Shuyuan Shang, Xiaopeng Wang +2
Neural operators serve as fast, data-driven surrogates for scientific modeling but typically rely on a monolithic, single-pass inference procedure that struggles to resolve high-fr…
HPC-Driven Modeling with ML-Based Surrogates for Magnon-Photon Dynamics in Hybrid Quantum Systems
Jialin Song, Yingheng Tang, Pu Ren +8
Simulating hybrid magnonic quantum systems remains a challenge due to the large disparity between the timescales of the two systems. We present a massively parallel GPU-based simul…
Modeling Non-Ergodic Path Effects Using Conditional Generative Model for Fourier Amplitude Spectra
Maxime Lacour, Pu Ren, Rie Nakata +2
Recent developments in non-ergodic ground-motion models (GMMs) explicitly model systematic spatial variations in source, site, and path effects, reducing standard deviation to 30-4…
WaveCastNet: Rapid Wavefield Forecasting for Earthquake Early Warning via Deep Sequence to Sequence Learning
Dongwei Lyu, Rie Nakata, Pu Ren +4
We propose a new deep learning model, WaveCastNet, to forecast high-dimensional wavefields. WaveCastNet integrates a convolutional long expressive memory architecture into a sequen…
Advancing data-driven broadband seismic wavefield simulation with multi-conditional diffusion model
Zhengfa Bi, Nori Nakata, Rie Nakata +3
Sparse distributions of seismic sensors and sources pose challenges for subsurface imaging, source characterization, and ground motion modeling. While large-N arrays have shown the…