Any convex L-Lipschitz functional on a compact convex subset of a separable Hilbert space can be uniformly approximated to arbitrary accuracy by an explicit convex L-Lipschitz reconstruction from finitely many linear measurements, exactly implementable by a ReLU-MLP.
Learning nonlinear operators via
3 Pith papers cite this work. Polarity classification is still indexing.
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HyCOP learns policies over compositions of hybrid modules to produce interpretable programs for parametric PDE solution operators with order-of-magnitude OOD gains over monolithic neural operators.
A constrained hypothesis-class framework for identifying mesoscopic dynamics from data, backed by uniform well-posedness and stability guarantees derived from a generalized Onsager principle.
citing papers explorer
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Structure-Preserving Reconstruction of Convex Lipschitz Functionals on Hilbert Spaces from Finite Samples
Any convex L-Lipschitz functional on a compact convex subset of a separable Hilbert space can be uniformly approximated to arbitrary accuracy by an explicit convex L-Lipschitz reconstruction from finitely many linear measurements, exactly implementable by a ReLU-MLP.
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HyCOP: Hybrid Composition Operators for Interpretable Learning of PDEs
HyCOP learns policies over compositions of hybrid modules to produce interpretable programs for parametric PDE solution operators with order-of-magnitude OOD gains over monolithic neural operators.
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Hypothesis-driven construction of mesoscopic dynamics
A constrained hypothesis-class framework for identifying mesoscopic dynamics from data, backed by uniform well-posedness and stability guarantees derived from a generalized Onsager principle.