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#artificial intelligence Preprint Sep 2026

Counterfactual Predictions in Scientific Emulators Without Controlled Experiments

Many scientific questions require reasoning about what was never observed: What if the conditions, interventions, or history had been different? Models can predict accurately on observed data yet fail on such what-if queries when correlated inputs are varied independently. A common remedy is to add controlled simulatio...

Ding-Ling Yao, Kahaan Gandhi, Valentin Duruisseaux et al. · 0 citations
#machine learning Preprint Sep 2026

A library for differentiable signal processing and machine learning on the sphere

The two-dimensional sphere embedded in three-dimensional Euclidean space S2, plays a central role in a variety of scientific and engineering domains, including geophysics, planetary science, geodesy, atmospheric physics, quantum chemistry, cosmology, and virtual reality, among many others. As machine learning increasin...

Thorsten Kurth, M. Rietmann, M. Bisson et al. · 0 citations
#machine learning Preprint Aug 2026

Accelerating Chemical Kinetics for Exoplanet Atmospheres using Neural Networks

The machine learning framework presented here is a flexible and efficient approach to emulating state-to-state flow-map problems that commonly arise in numerical simulations, and outperforms several commonly used machine learning architectures and performs robustly under the extreme stiffness characteristic of atmosphe...

Isaac Malsky, Xi Zhang, Tiffany Kataria et al. · 0 citations

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