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,
Canonical reference. 83% of citing Pith papers cite this work as background.
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83% of classified citations
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representative citing papers
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.
citing papers explorer
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Consistent Distributed Cooperative Localization for Ultra Large-Scale Multi-agent Systems
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.
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Nonlinear Stochastic Density Steering via Gaussian Mixture Schrodinger Bridges and Multiple Linearizations
Gaussian mixture models combined with multiple local linearizations solve nonlinear stochastic density steering and yield provably tighter approximation bounds than single-linearization baselines.
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State-space fading memory
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.
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Neural Network-Based Virtual Wheel-Speed Sensor for Enhanced Low-Velocity State Estimation
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.
- Stochastic Differential Dynamic Programming for Trajectory Optimization under Partial Observability