Scientific ML

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9 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
Jun 24, 2026Rikab Gambhir, Luisa Lucie-Smith, Jesse ThalerExplainable Artificial Intelligence Interpretable ML
Department of Physics, University of Cincinnati, Cincinnati, Ohio 45221, USA · Hamburger Sternwarte, Universität Hamburg, Gojenbergsweg 112, 21029 Hamburg,2026 Germany · Center for Theoretical Physics – a Leinweber Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USAJun +3
Machine Learning and Deep Learning for Exoplanet Detection and Atmospheric Characterization with JWST and the Upcoming Ariel Mission
Jun 22, 2026Muallim Yakubu, Vwavware Oruaode JudeScientific ML
Department of Industrial Physics, Enugu State University of Science and Technology · Department of Physics, Dennis Osadebay University, Asaba, Delta State, Nigeria
SPADE: Structure-Prior Adaptive Decision Estimation
Jun 22, 2026Yifan WangModel Selection Scientific ML
Department of Mechanical Engineering, McGill University · QC, H3A 2T7, Canada
ReLaTS: a Reinforcement Learning-based method for dynamically determining the coupling Time Step in multi-scale simulations of self-gravitating systems
Jun 18, 2026Veronica Saz Ulibarrena, Simon Portegies ZwartReinforcement Learning Scientific ML
Leiden Observatory, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
Statistical Properties of Training & Generalization
Jun 18, 2026Itay Lavie, Noam Levi, Yonatan KahnNeural Network Generalization Scientific ML
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA · Tel Aviv University, Tel Aviv, Israel · Department of Physics, University of Toronto and Vector Institute, Toronto, ON, Canada
Investigating Inductive Biases for Machine Learning Emulation of Sudden Stratospheric Warmings in Idealised Isca Simulations
Jun 17, 2026Oskar Bohn Lassen, Simon Driscoll, Stephen I. Thomson +2Neural Surrogate Modeling Climate Science
Department of Technology, Management, and Economics Technical University of Denmark · Department of Applied Mathematics and Theoretical Physics University of Cambridge · Department of Mathematics and Statistics University of Exeter
Modeling Doppler Shifts in Radial-Velocity Data with Deep Learning toward Earth-mass Exoplanet Detection
Jun 16, 2026Isidro Gómez-Vargas, Xavier Dumusque, Yinan Zhao +2Scientific ML
Department of Astronomy, University of Geneva 51 chemin de Pegasi, 1290 Versoix, Switzerland. · Instituto de Astrofıísica de Andalucía (CSIC), Glorieta de la Astronomía s/n, E-18008 Granada, Spain. · Institute of Space Sciences (CSIC), Carrer de Can Magrans s/n, E-08193 Barcelona, Spain. +3
Domain-Validity-Gated Metamorphic Testing of Scientific ML Surrogates
Jun 16, 2026Meng Li, Xiaohua Yang, Jie Liu +1Metamorphic Testing Scientific ML
School of Computing, University of South China, Hengyang, China · Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment, Hengyang, China · CNNC Key Laboratory on High Trusted Computing, Hengyang, China
SciML in the Wild: A Diagnostic Study of When Structural Priors Help and When They Hurt
Jun 15, 2026Vrishank Sai Anand, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar +2Time Series Forecasting Scientific ML
GEMS Modern Academy Dubai, United Arab Emirates · Vizuara AI Labs, Pune, India
Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis
Jun 8, 2026Mikele Milia, Louis Fabrice Tshimanga, Henning Mueller +2Transformer Interpretability Transformer Attention
Department of Information Engineering, University of Padova, Padova, Italy · Department of Neuroscience, University of Padova, Padova, Italy · Padova Neuroscience Center, Padova, Italy +3
From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models
Jun 8, 2026Conor RowanScientific Discovery Scientific ML
Aerospace Engineering2026 University of Colorado Boulder 3775 Discovery DriveJun 9 Boulder, CO 80309
Instrumented data for causal scientific machine learning
Jun 5, 2026Daniel N. WilkeScientific ML
School of Mechanical, Industrial and Aeronautical Engineering, University of the Witwatersrand, Johannesburg, South Africa
Implicit Data Synthesis for Contrastive Unsupervised Data Augmentation
Jun 5, 2026Patrick Kage, Trevor Hedges, N. Siddharth +1Contrastive Learning Synthetic Data Augmentation
School of Informatics The University of Edinburgh Edinburgh, Scotland EH8 9AB UK · Massachusetts Institute of Technology Lincoln Laboratory Lexington, MA 02421 USA
Synthics: Synthetic Physics-like Datasets for Machine Learning
Jun 4, 2026Jari VepsäläinenSynthetic Data Generation Scientific ML
Mechatronics Department of Energy and Mechanical Engineering School of Engineering Aalto University Finland
Reconstructing Unobservable Temperature Fields via Simulation-Aided Intelligent Sensing
Jun 3, 2026Monika Stipsitz, Hèlios Sanchis-Alepuz, Jacob Reynvaan +1Synthetic Data Generation Scientific ML
Embedded Systems Silicon Austria Labs GmbH Graz, Austria
DiffUNet^2: Bidirectional Conditional Diffusion for Probabilistic Scientific Spatiotemporal Modeling
Jun 2, 2026Mengdi Chu, Jiaxin Yang, Angus G. Forbes +4Diffusion Model Guidance Probabilistic Forecasting
Mengdi Chu, Jiaxin Yang, and Han-Wei Shen are with the Ohio State University. · Angus G. Forbes is with NVIDIA. · Nathan Debardeleben, Earl Lawrence, Ayan Biswas are with Los Alamos National Laboratory.
A Geometric Lens on Physics-Aligned Data Compression
Jun 2, 2026Aleix Segui, Wesley ArmourRate-Distortion Theory Rate-Distortion Optimization
Department of Engineering Science, University of Oxford, Oxford, UK.
RobustModelMaker: Coupling Bootstrap Stability Selection with Leakage-Safe Nested Cross-Validation for Scientific Machine Learning
Jun 1, 2026Amanda S BarnardData Leakage Scientific ML
School of Computing Australian National University Acton, ACT 2601
Cellular Sheaf Neural Operators for Structure-Preserving Surrogate Modeling of Constrained PDEs
May 31, 2026Lennon J. Shikhman, Shane GilbertiePDE Surrogate Modeling Scientific ML
College of Computing, Georgia Institute of Technology, Atlanta, 30332, GA, USA · Department of Aerospace, Physics and Space Sciences, Florida Institute of Technology, Melbourne, 32901, FL, USA
Position: Prioritize Identifying Structure, Not Complex Models, for Scientific Discovery
May 30, 2026Tyler H. McCormickCausal Identifiability Scientific ML
Departments of Statistics and Sociology, University of Washington, Seattle, WA, USA.
Data-Driven Spectral Prediction for Accelerating Large-Scale Electronic Structure Calculations
May 29, 2026Abhiram Badrinarayanan, Davor Davidovic, Edoardo Di Napoli +4Quantum Chemistry Scientific ML
Jülich Supercomputing Centre, Forschungszentrum Jülich, Germany · CEA / IRIG / MEM / L_Sim, France
Scientific Machine Learning for Engine Health Management and Remaining Useful Life Prediction
May 28, 2026Jostein Barry-Straume, Changmin Son, Adrian Sandu +4RUL Estimation Predictive Maintenance
Computational Science Laboratory Department of Computer Science Virginia Tech Blacksburg, VA 24060
Unveiling Multi-regime Patterns in SciML: Distinct Failure Modes and Regime-specific Optimization
May 27, 2026Yuxin Wang, Yuanzhe Hu, Xiaokun Zhong +7Neural Network Optimization Scientific ML
Dartmouth College · University of California, San Diego · University of California, Berkeley +3
The Perception-Physics Paradox: Probing Scientific Alignment with TC-Bench
May 23, 2026Dingling Yao, Andrea Polesello, Adeel Pervez +2Representation Learning Vision Foundation Models
Institute of Science and Technology Austria
The Neural Compiler: Program-to-Network Translation for Hybrid Scientific Machine Learning
May 21, 2026Lucas ShenemanDifferentiable Programming Scientific ML
Institute for Interdisciplinary Data Sciences University of Idaho
Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid
May 18, 2026Aritra Ghosh, Drew Oldag, Michael Tauraso +29Scientific ML
Dept. of Astronomy & the DiRAC Institute, University of Washington, Box 351580, Seattle, WA 98195, USA · eScience Institute, University of Washington, Seattle, WA, U.S.A. · School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455, USA +6
Can machine learning for quantum-gas experiments be explainable?
May 18, 2026I. B. Spielman amd J. P. ZwolakInterpretable ML Scientific ML
National Institute of Standards and Technology, Gaithersburg, MD 20899, USA · Department of Physics, University of Maryland, College Park, MD 20742, USA · Joint Quantum Institute, University of Maryland, College Park, MD 20742, USA
Buffer-Parameterized Machine Learning Surrogate Models for Cross-Technology Signal Integrity Analysis and Optimization
May 18, 2026Julian Withöft, Werner John, Emre Ecik +2Neural Surrogate Modeling Surrogate-Assisted Optimization
Information Processing Lab, Faculty for Electrical Engineering and Information Technology, TU Dortmund, Germany · Pyramide2525, Paderborn, Germany · EMC Technology Center Paderborn, Zuken GmbH, Paderborn, Germany
Mechanism Learning: Prototype-Anchored Mechanism Inference for Scientific Forecasting
May 16, 2026Qian Jiang, Liping SunPrototype-Based Learning Non-Stationary Time Series Forecasting
School of Computing · The Australian National University · Canberra, ACT 2601, Australia +3
CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models
May 15, 2026Stefano Riva, Carolina Introini, Antonio Cammi +13Surrogate Modeling Scientific ML
Autodesk Research, London, UK · Department of Energy, Nuclear Engineering Division, Politecnico di Milano, Milan, Italy · Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139 +10

Jun 24, 2026, physics.data-anJ/K move · Enter open · S save

Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics

Rikab Gambhir, Luisa Lucie-Smith, Jesse Thaler

Department of Physics, University of Cincinnati, Cincinnati, Ohio 45221, USA · Hamburger Sternwarte, Universität Hamburg, Gojenbergsweg 112, 21029 Hamburg,2026 Germany · Center for Theoretical Physics – a Leinweber Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USAJun+3

Explainable Artificial Intelligence Interpretable ML Scientific ML

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We review the concepts of interpretability and explainability as they apply to machine learning in physics. We define interpretability as concerning the structural transparency of a model (the ability to understand or approximate its inner workings) and explainability as concerning the scientific content of a model (the ability to map it onto domain knowledge). We discuss the trade-offs each entails (interpretability vs. expressivity; explainability vs. adaptability), the contexts in which each is needed, and the intrinsic and post-hoc tools available for achieving them. Throughout, we emphasize that machine-learned models are subject to the same scientific questions as classical models, differing only in scale, and that interpretability and explainability are best understood as deliberate modeling choices rather than inherent properties. We also emphasize the importance of task specification and intervention plans as a core aspect of model design.

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