#hybrid-modeling News & Analysis

2 articles tagged with #hybrid-modeling. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · Jun 57/10

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Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming

Researchers propose hybrid computational models combining mechanistic physics-based solvers with deep learning to improve neurological disorder diagnosis and treatment planning. These integrative approaches—using residual modeling, Neural ODEs, and solver-in-the-loop architectures—overcome limitations of purely mechanistic or data-driven methods alone, demonstrating superior performance in modeling brain tumors, Alzheimer's disease, and stroke progression.

AINeutralarXiv – CS AI · Jun 236/10

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Where Is My Physics Wrong? Localized and Identifiable Discovery of Model Discrepancy

Researchers introduce LISDD, a framework for identifying where and why physics-based models fail by localizing errors to specific operating regimes and discovering sparse symbolic corrections. The method outperforms existing global-correction approaches by keeping parameter bias near zero while maintaining statistical rigor through finite-sample testing.