arXiv:2609.23381v1 Announce Type: new Abstract: Before a machine can discover a physical law, it must discover what its measurements are: which observations live on cells, which are intensive or exte...
arXiv:2608. 02662v1 Announce Type: cross Abstract: Reliable forecasting of nonlinear physical systems underpins scientific discovery and engineering decision-making.
arXiv:2608. 05702v1 Announce Type: new Abstract: Scientific machine learning commonly validates models at the level of a subdomain, a benchmark split, or an explanation for one prediction.
arXiv:2511. 08860v2 Announce Type: replace-cross Abstract: The deep learning revolution has spurred a rise in advances of using AI in sciences.
arXiv:2606. 07563v1 Announce Type: cross Abstract: Across machine learning, biology, and physics, independently evolving systems often converge toward strikingly similar high-level structures despite radically different microscopic details.
arXiv:2608. 12624v1 Announce Type: new Abstract: Structure-preserving machine learning embeds physical structure directly into model architectures, yet uncertainty quantification (UQ) for such hard-constrained models remains limited because standard UQ methods may violate the encoded admissibility conditions, require architectural modifications, or impose substantial computational costs.
arXiv:2608. 15645v1 Announce Type: new Abstract: Transporting a causal conclusion from a source study population to a target one is a fundamental problem in causal inference.
arXiv:2607. 05436v1 Announce Type: new Abstract: The rapid scaling of over-parameterized machine learning architectures, particularly LLMs, raises a profound crisis: do these systems exhibit genuine intelligence, or are they merely sophisticated statistical pattern matchers?
The paper introduces Resolution-Aware Experimental Design (RAED), a method that selects experiments by minimizing the expected size of the nonempty structural candidate set while controlling false-exclusion rates. RAED is shown to preserve expected ordering under a composite Blackwell comparison and is implemented via a learned score-based approach with finite-sample nuisance-average and positive-tail calibration. Experiments on subsurface-flow, fluvial, and methane-oxidation benchmarks demonstrate RAED’s ability to resolve structural ambiguities and provide finite-sample guarantees for tail-sensitive nuisance risk.
arXiv:2606. 30064v1 Announce Type: new Abstract: We introduce a data-driven probabilistic framework for learning systems based on Gibbs measures on hierarchical structures.
The paper introduces Resolution-Aware Experimental Design (RAED), a method that selects experiments by minimizing the expected size of the nonempty structural candidate set while controlling false exclusions. RAED is shown to align with a composite Blackwell comparison and is implemented via a learned score-based approach with finite-sample calibration. Experiments on subsurface-flow, fluvial, and methane-oxidation benchmarks demonstrate that RAED can diverge from expected-information-gain selections, yielding clearer resolution and explicit ambiguity handling.
arXiv:2607. 03513v1 Announce Type: cross Abstract: We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations from the Boltzmann distribution at a fraction of the cost of molecular dynamics (MD).