Sparsely-Supervised Data Assimilation via Physics-Informed Schr\"odinger Bridge
📰 ArXiv cs.AI
Physics-Informed Schr"odinger Bridge enables sparsely-supervised data assimilation for systems governed by partial differential equations
Action Steps
- Formulate the data assimilation problem as a Schr"odinger bridge problem
- Use physics-informed neural networks to model the governing partial differential equations
- Implement the physics-informed Schr"odinger bridge algorithm to assimilate sparse high-fidelity observations with low-fidelity simulations
- Evaluate the performance of the proposed approach using metrics such as accuracy and computational efficiency
Who Needs to Know This
Data scientists and AI engineers working on multi-fidelity data assimilation problems can benefit from this approach, as it allows for accurate reconstruction of high-fidelity fields from sparse observations
Key Insight
💡 The proposed approach enables accurate reconstruction of high-fidelity fields from sparse observations while respecting physical constraints
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🚀 Physics-Informed Schr"odinger Bridge for sparsely-supervised data assimilation! 📊
Key Takeaways
Physics-Informed Schr"odinger Bridge enables sparsely-supervised data assimilation for systems governed by partial differential equations
Full Article
Title: Sparsely-Supervised Data Assimilation via Physics-Informed Schr\"odinger Bridge
Abstract:
arXiv:2603.22319v1 Announce Type: cross Abstract: Data assimilation (DA) for systems governed by partial differential equations (PDE) aims to reconstruct full spatiotemporal fields from sparse high-fidelity (HF) observations while respecting physical constraints. While full-grid low-fidelity (LF) simulations provide informative priors in multi-fidelity settings, recovering an HF field consistent with both sparse observations and the governing PDE typically requires per-instance test-time optimiz
Abstract:
arXiv:2603.22319v1 Announce Type: cross Abstract: Data assimilation (DA) for systems governed by partial differential equations (PDE) aims to reconstruct full spatiotemporal fields from sparse high-fidelity (HF) observations while respecting physical constraints. While full-grid low-fidelity (LF) simulations provide informative priors in multi-fidelity settings, recovering an HF field consistent with both sparse observations and the governing PDE typically requires per-instance test-time optimiz
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