Generalized Multi-source Assimilation: A Framework for Cross-Modal Integration and Source Optimization

Pujan Pokhrel

doi:10.1016/j.jcp.2026.115297

Generalized Multi-source Assimilation: A Framework for Cross-Modal Integration and Source Optimization

2026-08-22

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DOI：10.1016/j.jcp.2026.115297

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This work establishes the theoretical foundations for optimal weight learning in multi-source scientific machine learning. By moving away from empirical, ad-hoc weighting, our framework explores the automated and mathematically sound fusion of diverse numerical methods, neural architectures, and noisy measurements. We analyze a comprehensive spectrum of coupling mechanisms, ranging from unconstrained Softmax parameterization to explicitly constrained formulations like ADMM, as well as single-timescale, two-timescale, and adaptive-ρ Augmented Lagrangian methods.

Generalized Multi-source Assimilation: A Framework for Cross-Modal Integration and Source Optimization

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