Adaptive MFML algorithm saturates accuracy at low fidelities before escalating, cutting data costs up to 30x vs single-fidelity and 5x vs standard MFML on coupled cluster and excitation energies.
Daniel and Schaefer, Henry F
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
A reorganized Hartree-Fock framework imposes tunable orbital locality by pairing local degrees of freedom with local solution conditions, maintaining efficient SCF optimization and competitive reaction-energy accuracy.
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.
citing papers explorer
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Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning
Adaptive MFML algorithm saturates accuracy at low fidelities before escalating, cutting data costs up to 30x vs single-fidelity and 5x vs standard MFML on coupled cluster and excitation energies.
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Approximating Hartree-Fock theory via an efficiently local reformulation
A reorganized Hartree-Fock framework imposes tunable orbital locality by pairing local degrees of freedom with local solution conditions, maintaining efficient SCF optimization and competitive reaction-energy accuracy.
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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.