RoverDevKit is an open physics-based evaluator for lunar micro-rover conceptual design that runs in 30 ms and uses NSGA-II to identify mission-dependent optimal wheel configurations and binding trades.
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Golub, Michael Heath, and Grace Wahba
14 Pith papers cite this work. Polarity classification is still indexing.
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A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
Presents a quantum soft PCA framework with Fermi-Dirac filter for principal subspace scoring without eigenvector recovery, claiming dimension-independent sample complexity O(η^{-2}).
A geometry-aligned bi-fidelity surrogate maps low- and high-fidelity wildfire solutions to a common domain for improved reduced-basis reconstruction, lower error near fronts, and practical uncertainty quantification.
pKANrtm uses a physics-aware multi-fidelity KAN to emulate high-fidelity radiative transfer coefficients for atmospheric correction with superior accuracy and large speedups over direct libRadtran runs.
Kolmogorov n-width theory plus PRESS statistics yield closed-form optimal spline resolution; KORE estimates bias/noise scales from two pilots and matches CV performance with far fewer fits.
TCP-MCP co-evolves prompts and topologies for multi-agent systems, reporting 82.66-96.61% accuracy on MMLU-Pro/MMLU/GSM8K while using up to 5.69x fewer tokens than debate baselines.
Orthogonal reparametrization via QR decomposition renders NSS linear parameters uncorrelated with diagonal conditional Fisher information, providing a scalar identifiability diagnostic and closed-form finite-horizon orthogonal basis.
BERT activations show strongest correlation with MEG data for simple sentences; DNN representations generate synthetic brain data that improves stimuli decoding accuracy.
LoRA adapters enable a 61.47M-parameter aerodynamics Transformer pretrained on four vehicle families to adapt to a held-out fifth family with 20 samples, reaching R²=0.85 and outperforming full fine-tuning and from-scratch training with 3x more data.
BG-SINDy reformulates l0-constrained regression as term-level l2,0 regularization and uses progressive pruning guided by balance contributions to recover small-coefficient terms in multiscale PDEs.
Recursive multi-fidelity GP regression with EM optimization trains faster than the coupled non-recursive Kennedy-O'Hagan approach on noisy non-nested data while delivering comparable predictions and uncertainty estimates.
Modifies Gibbs sampler for GP state-space models, introduces CFA measurement structure, and validates software via simulation-based calibration to enable reliable learning of nonlinear latent dynamics.
PINN achieves 91% accuracy in 3D noisy heat diffusion vs 36% for FDM and 3.3x better error reduction in physical experiment, with efficiency gains in high dimensions.
citing papers explorer
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RoverDevKit: An open, physics-grounded tradespace toolkit for conceptual design of lunar micro-rovers
RoverDevKit is an open physics-based evaluator for lunar micro-rover conceptual design that runs in 30 ms and uses NSGA-II to identify mission-dependent optimal wheel configurations and binding trades.
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Bayesian Global Fr\'echet Regression via Weak Conditional Expectations
A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
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Quantum principal component analysis without eigenvector recovery
Presents a quantum soft PCA framework with Fermi-Dirac filter for principal subspace scoring without eigenvector recovery, claiming dimension-independent sample complexity O(η^{-2}).
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A geometry-aligned multi-fidelity framework for uncertainty quantification of wildfire spread
A geometry-aligned bi-fidelity surrogate maps low- and high-fidelity wildfire solutions to a common domain for improved reduced-basis reconstruction, lower error near fronts, and practical uncertainty quantification.
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Multi-Fidelity Emulation of Atmospheric Correction Coefficients with Physics-Guided Kolmogorov-Arnold Networks
pKANrtm uses a physics-aware multi-fidelity KAN to emulate high-fidelity radiative transfer coefficients for atmospheric correction with superior accuracy and large speedups over direct libRadtran runs.
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Solve for the Hyperparameter, Skip the Search: Kolmogorov-Optimal Scaling Laws for Spline Regression
Kolmogorov n-width theory plus PRESS statistics yield closed-form optimal spline resolution; KORE estimates bias/noise scales from two pilots and matches CV performance with far fewer fits.
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TCP-MCP: Landscape-Guided Co-Evolution of Prompts and Communication Topologies for Multi-Agent Systems
TCP-MCP co-evolves prompts and topologies for multi-agent systems, reporting 82.66-96.61% accuracy on MMLU-Pro/MMLU/GSM8K while using up to 5.69x fewer tokens than debate baselines.
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Orthogonal reparametrization of the Nelson-Siegel-Svensson interest rate curve model: conditioning, diagnostics, and identifiability
Orthogonal reparametrization via QR decomposition renders NSS linear parameters uncorrelated with diagonal conditional Fisher information, providing a scalar identifiability diagnostic and closed-form finite-horizon orthogonal basis.
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Relating Simple Sentence Representations in Deep Neural Networks and the Brain
BERT activations show strongest correlation with MEG data for simple sentences; DNN representations generate synthetic brain data that improves stimuli decoding accuracy.
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Adapting Automotive Aerodynamics Surrogates to New Vehicle Families via Transfer Learning
LoRA adapters enable a 61.47M-parameter aerodynamics Transformer pretrained on four vehicle families to adapt to a held-out fifth family with 20 samples, reaching R²=0.85 and outperforming full fine-tuning and from-scratch training with 3x more data.
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Balance-Guided Sparse Identification of Multiscale Nonlinear PDEs with Small-coefficient Terms
BG-SINDy reformulates l0-constrained regression as term-level l2,0 regularization and uses progressive pruning guided by balance contributions to recover small-coefficient terms in multiscale PDEs.
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Multi-fidelity Gaussian process regression for noisy outputs and non-nested experimental designs: a comparison between the recursive and non-recursive formulations
Recursive multi-fidelity GP regression with EM optimization trains faster than the coupled non-recursive Kennedy-O'Hagan approach on noisy non-nested data while delivering comparable predictions and uncertainty estimates.
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Learning Nonlinear Dynamics: Improving the Estimation Efficiency and Reliability of Gaussian Process State-Space Models
Modifies Gibbs sampler for GP state-space models, introduces CFA measurement structure, and validates software via simulation-based calibration to enable reliable learning of nonlinear latent dynamics.
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Overcoming the Limits of Finite Difference Method; Physics-Informed Neural Network for Noisy High-Dimensional Heat Diffusion
PINN achieves 91% accuracy in 3D noisy heat diffusion vs 36% for FDM and 3.3x better error reduction in physical experiment, with efficiency gains in high dimensions.