PVD-ONet combines multi-network DeepONet modules with Prandtl and Van Dyke matching conditions to map initial data to solution operators for families of singularly perturbed boundary-layer problems and to infer scaling exponents from sparse observations.
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Multi-agent LLM system applies set-based design and Conditional Value-at-Risk to explore and risk-filter airfoil designs with human manager coordination.
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PVD-ONet: A Multi-scale Neural Operator Method for Singularly Perturbed Boundary Layer Problems
PVD-ONet combines multi-network DeepONet modules with Prandtl and Van Dyke matching conditions to map initial data to solution operators for families of singularly perturbed boundary-layer problems and to infer scaling exponents from sparse observations.
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Agentic Risk-Aware Set-Based Engineering Design
Multi-agent LLM system applies set-based design and Conditional Value-at-Risk to explore and risk-filter airfoil designs with human manager coordination.