An LLM-driven agent with built-in seed-noise audits develops control policies for two aerospace problems that outperform undirected search and pass verification checks.
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Learning latent representations in high-dimensional state spaces using polynomial manifold constructions,
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SparseModesNet uses POD linear encoding with LassoNet-enforced sparse nonlinear NN decoding to select modes and reduce reconstruction error by 51-78% versus polynomial manifold methods on turbulent channel flow while preserving interpretability.
FastQM rotates a candidate basis of singular vectors on the Stiefel manifold to maximize quadratic manifold approximation quality, with feature-space cost independent of full dimension, shown on turbulent airfoil-wake data.
OCULAR calibrates dynamics uncertainty using perception from similar environments to give guaranteed prediction regions for unseen test conditions.
A new cooperative localization algorithm based on overlapping covariance intersection is fully distributed, provably recursively consistent, and scalable to ultra large-scale multi-agent systems without performance loss from ignored cross-correlations.
Proposes adaptive multiple importance sampling for robust Bayesian model evidence estimation under parameter non-identifiability, shown to outperform deterministic methods on ecological case studies while being cheaper than MCMC.
Augmented Krylov subspaces jointly approximate quadratic forms and log-dets for faster MLE-based hyperparameter tuning in kernel-based linear system identification.
Gaussian mixture models combined with multiple local linearizations solve nonlinear stochastic density steering and yield provably tighter approximation bounds than single-linearization baselines.
A state-space definition of fading memory is introduced that extends incremental input-to-output stability via a memory kernel, is implied by incremental input-to-state stability under bounded inputs, and holds for current-driven memristor models.
An autopilot-preserving residual Q-learning supervisor with HJB-inspired finite-action risk filtering reduces mean RMS path-tracking error from 338.617 m to 44.809 m (86.77% reduction) in fixed simulation benchmarks.
Reinforcement learning learns a policy that adapts control parameters of a regularized interior-point method, accelerating high-accuracy solutions for convex quadratic programs and generalizing across problem classes after lightweight training.
A neural network fuses wheel and motor speed signals to cut wheel-speed estimation error by up to 85% versus the production sensor on real Volkswagen ID.7 data.
A survey that taxonomizes threats to agentic AI, reviews benchmarks and evaluation methods, discusses technical and governance defenses, and identifies open challenges.
A systematic mapping study of Karma mechanisms that compares applications, structures design parameters, and maps future research directions in non-monetary resource allocation.
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Sparse POD Mode Selection and Manifold Dimensionality Reduction with Neural Networks
SparseModesNet uses POD linear encoding with LassoNet-enforced sparse nonlinear NN decoding to select modes and reduce reconstruction error by 51-78% versus polynomial manifold methods on turbulent channel flow while preserving interpretability.