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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Frequency-adaptive tensor neural networks are proposed to overcome the frequency principle in TNNs for high-dimensional multi-scale problems by incorporating random Fourier features and 1D DFT on component functions.
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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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Frequency-adaptive tensor neural networks for high-dimensional multi-scale problems
Frequency-adaptive tensor neural networks are proposed to overcome the frequency principle in TNNs for high-dimensional multi-scale problems by incorporating random Fourier features and 1D DFT on component functions.
- ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms