GRAFT-ATHENA projects combinatorial method choices into factored trees that embed as fingerprints in a metric space, enabling an agentic system to accumulate experience across domains and autonomously discover new numerical techniques for physics-informed problems.
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RA-PINN embeds gated attention in a residual network to reduce localized errors at steep charge boundaries while obeying the governing equations.
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GRAFT-ATHENA: Self-Improving Agentic Teams for Autonomous Discovery and Evolutionary Numerical Algorithms
GRAFT-ATHENA projects combinatorial method choices into factored trees that embed as fingerprints in a metric space, enabling an agentic system to accumulate experience across domains and autonomously discover new numerical techniques for physics-informed problems.
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High-Fidelity Reconstruction of Charge Boundary Layers and Sharp Interfaces in Electro-Thermal-Convective Flows via Residual-Attention PINNs
RA-PINN embeds gated attention in a residual network to reduce localized errors at steep charge boundaries while obeying the governing equations.