Neural equal-area scattering surrogate for He-Ar preserves QD, Qμ, RCS and related quantities within 1.5% and reproduces DSMC diffusion and shear tests within 1-2% normalized error.
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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.
Formalizes a semigroup structure on directed paths in a templex (the 'generatex' semigroup), giving new functorial invariants of chaotic dynamics that go beyond homological and metric information.
PINNs trained on fluid sheath equations produce parametric surrogate models that predict plasma sheath profiles across ion species, temperature ratios, and collisionalities, validated against Runge-Kutta solvers.
A review summarizing machine learning methods for multi-messenger probes of dark matter and new physics, with a proposed plan for future integrated analyses.
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Transport-preserving neural ab initio scattering kernels for rarefied binary gas mixtures
Neural equal-area scattering surrogate for He-Ar preserves QD, Qμ, RCS and related quantities within 1.5% and reproduces DSMC diffusion and shear tests within 1-2% normalized error.
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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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Functorial invariants for chaos topology from data
Formalizes a semigroup structure on directed paths in a templex (the 'generatex' semigroup), giving new functorial invariants of chaotic dynamics that go beyond homological and metric information.
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A Deep Learning Approach to Describing the Plasma Sheath
PINNs trained on fluid sheath equations produce parametric surrogate models that predict plasma sheath profiles across ion species, temperature ratios, and collisionalities, validated against Runge-Kutta solvers.
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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.