Category-theoretic functorial invariants built from directed path algebras on templexes yield separable homology and semigroup structures that detect chaos and tipping points from data.
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5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 5verdicts
UNVERDICTED 5representative citing papers
GQPINNs add symmetry awareness to quantum PINNs via equivariant circuits, yielding lower mean absolute error and fewer parameters than standard QPINNs on linear and nonlinear PDE benchmarks.
DC-PINNs embed derivative constraints into PINN optimization using a minimum principle and adaptive balancing, reducing violations and improving fidelity on heat, finance, and fluid benchmarks.
A physics-informed neural network acts as an efficient surrogate solver for fluid models of the plasma sheath across parameter regimes.
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.
citing papers explorer
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Functorial invariants for chaos topology from data
Category-theoretic functorial invariants built from directed path algebras on templexes yield separable homology and semigroup structures that detect chaos and tipping points from data.
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Geometric Quantum Physics Informed Neural Network
GQPINNs add symmetry awareness to quantum PINNs via equivariant circuits, yielding lower mean absolute error and fewer parameters than standard QPINNs on linear and nonlinear PDE benchmarks.
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Physics-Informed Neural Networks for Solving Derivative-Constrained PDEs
DC-PINNs embed derivative constraints into PINN optimization using a minimum principle and adaptive balancing, reducing violations and improving fidelity on heat, finance, and fluid benchmarks.
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A Deep Learning Approach to Describing the Plasma Sheath
A physics-informed neural network acts as an efficient surrogate solver for fluid models of the plasma sheath across parameter regimes.
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Machine Learning for Multi-messenger Probes of New Physics and Cosmology: A Review and Perspective
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.