arXiv AI
Jun 26
Socratic agents for autonomous scientific discovery in high-dimensional physical systems
arXiv:2606. 26722v1 Announce Type: new Abstract: The automation of scientific discovery has reached an inflection point.
From Closed-Loop Optimization to Open Decision Making: Coupled Digital Twins for Predictive and Autonomous Microscopy
arXiv:2607. 05758v1 Announce Type: cross Abstract: Automated experimentation is moving from closed-loop optimization toward open decision-making, where human or AI planners must forecast the consequences of candidate actions before executing them.
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arXiv Machine Learning
Jul 1
Quality-Controlled Active Learning via Gaussian Processes for Robust Structure-Property Learning in Autonomous Microscopy
arXiv:2603. 29135v2 Announce Type: replace Abstract: Autonomous experimental systems are increasingly used in materials research to accelerate scientific discovery, but their performance is often limited by low-quality, noisy data.
arXiv Machine Learning
Aug 11
Multitask Scanning Probe Microscopy
arXiv:2608. 09104v1 Announce Type: cross Abstract: Scanning probe microscopy provides nanoscale access to structural, electrical, electromechanical, magnetic, and mechanical properties of materials.
arXiv AI
Jul 21
A Continual Validation, Updating, and Decision-Making Framework for Self-Adaptive Digital Twins via Robust Model Predictive Control: A Case Study in Additive Manufacturing
arXiv:2607. 18164v1 Announce Type: cross Abstract: Digital Twins rely on surrogate models to mirror physical systems in real time, yet these models can degrade as operating conditions evolve, a phenomenon known as concept drift.
arXiv Machine Learning
Jul 7
Modular Foundation Models for Time-Series Perception in Digital Twins
arXiv:2607. 03585v1 Announce Type: new Abstract: Engineering Digital Twins and Prognostics and Health Management (PHM) systems rely on robust perception modules to extract actionable information from heterogeneous and non-stationary time-series data.
arXiv Machine Learning
Jun 16
Physics-conforming Latent Twins
arXiv:2606. 15053v1 Announce Type: new Abstract: Surrogate models are central to scientific machine learning, where they enable fast prediction, simulation, inference, and control for complex physical systems.
arXiv AI
Jul 24
A Graph Neural Network approach to zero-shot Digital Twins
arXiv:2607. 20535v1 Announce Type: cross Abstract: Traditional Predictive Digital Twins often remain geometrically rigid, requiring extensive retraining or fine-tuning whenever the underlying physical domain or boundary conditions change.
arXiv Machine Learning
Aug 11
Trajectory Design and Budgeted Querying for Digital Twin Calibration
arXiv:2608. 08631v1 Announce Type: new Abstract: Digital-twin calibration requires interaction data that is expensive to collect.
arXiv AI
Aug 7
Agentic self-driving microscopy benchmarks support qualification but do not necessarily generalize to unseen tasks
arXiv:2608. 05266v1 Announce Type: new Abstract: Large language model agents are increasingly being developed to control a wide range of scientific characterization tools including microscopes and synchrotron beamlines.
arXiv Machine Learning
Jun 24
3D Masked Autoencoders are Robust Learners of Volumetric and Multimodal Cellular Representations for Microscopy
arXiv:2606. 23964v1 Announce Type: new Abstract: Self-supervised learning in fluorescence microscopy often relies on 2D projections, despite the inherently three-dimensional nature of cells.
arXiv AI
Jun 16
EgoPhys: Learning Generalizable Physics Models of Deformable Objects from Egocentric Video
arXiv:2606. 16202v1 Announce Type: cross Abstract: Humans naturally understand object physics through everyday interactions, but faithfully predicting complex deformable dynamics, such as elastic materials and fabrics, remains a major challenge for computer vision and robotics.
arXiv AI
Jul 21
Autonomous mechanistic discovery of colorectal cancer vulnerabilities via multi-scale AI swarms
arXiv:2607. 16262v1 Announce Type: cross Abstract: The acceleration of automated scientific discovery has been fundamentally bottlenecked by the epistemic gap between the semantic reasoning of large language models (LLMs) and the deterministic physics of mammalian biology.