CUTS-GPR performs numerically exact Gaussian process regression with near-linear scaling in training points N and low-order polynomial scaling in dimensions D by exploiting additive kernels on incomplete grids.
Generalized Hamiltonian dynamics,
4 Pith papers cite this work, alongside 2,074 external citations. Polarity classification is still indexing.
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A least-constraint variational principle is introduced for quantum mechanics in which minimizing a probability-weighted acceleration-deviation functional, with the quantum potential as constraint, yields equations equivalent to the Schrödinger equation.
Frontier LLMs with in-context learning and CAS integration solve most algorithmic tasks in theoretical physics when supplied with worked examples.
The conformal null string reduces from d+2 to d dimensions via Dirac slices, with the Virasoro-su(1,1) algebra mapping to Carrollian-Weyl symmetry.
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Don't Get Your Kroneckers in a Twist: Gaussian Processes on High-Dimensional Incomplete Grids
CUTS-GPR performs numerically exact Gaussian process regression with near-linear scaling in training points N and low-order polynomial scaling in dimensions D by exploiting additive kernels on incomplete grids.
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Least constraint approach to non-relativistic quantum mechanics
A least-constraint variational principle is introduced for quantum mechanics in which minimizing a probability-weighted acceleration-deviation functional, with the quantum potential as constraint, yields equations equivalent to the Schrödinger equation.
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LLMs with in-context learning for Algorithmic Theoretical Physics
Frontier LLMs with in-context learning and CAS integration solve most algorithmic tasks in theoretical physics when supplied with worked examples.
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The conformal null string in $d+2$ and $d$ dimensions
The conformal null string reduces from d+2 to d dimensions via Dirac slices, with the Virasoro-su(1,1) algebra mapping to Carrollian-Weyl symmetry.