Presentation
Neural Domain Decomposition for Scalable Multi-Physics: Chip-Scale Thermal-Stress Analysis
DescriptionLarge neural surrogate models accelerate scientific simulation but still face scalability limits from model size and memory.
We present FP-DDM (Foundation-model-based Physics-guided adaptation for the Domain Decomposition Method), a scalable framework for large-scale physics analysis. FP-DDM divides the domain into overlapping subdomains and enforces interface consistency by explicitly optimizing boundary values on overlaps via automatic differentiation, enabling fast alignment. Specifically, we propose a foundation architecture that effectively learns multi-physics and trasfer new domain.
The combination of foundation-model adaptation and physics-guided test-time adaptation enables generalization to new physics and unseen domains without labels. On thermal and stress problems, FP-DDM achieves numerical-solver-level accuracy, scales to 10-billion-cell domains, and supports layout design optimization, establishing its potential as a multi-physics platform for next-generation System–Technology Co-Optimization.
We present FP-DDM (Foundation-model-based Physics-guided adaptation for the Domain Decomposition Method), a scalable framework for large-scale physics analysis. FP-DDM divides the domain into overlapping subdomains and enforces interface consistency by explicitly optimizing boundary values on overlaps via automatic differentiation, enabling fast alignment. Specifically, we propose a foundation architecture that effectively learns multi-physics and trasfer new domain.
The combination of foundation-model adaptation and physics-guided test-time adaptation enables generalization to new physics and unseen domains without labels. On thermal and stress problems, FP-DDM achieves numerical-solver-level accuracy, scales to 10-billion-cell domains, and supports layout design optimization, establishing its potential as a multi-physics platform for next-generation System–Technology Co-Optimization.
Event Type
Research Manuscript
TimeMonday, July 2711:23am - 11:36am PDT
LocationMtg Room 101A
Similar Presentations