Bifrost: a Python/C++ Framework for High-Throughput Stream Processing in Astronomy. - Miles D. Cranmer; Benjamin R. Barsdell; Danny C. Price; Jayce Dowell; Hugh Garsden; Veronica Dike; Tarraneh Eftekhari; Alexander M. Hegedus; Joseph Malins; Kenneth S. Obenberger; Frank Schinzel; Kevin Stovall; et al. (CoRR, 2017)

Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates. - Miles D. Cranmer; Richard Galvez; Lauren Anderson; David N. Spergel; Shirley Ho (CoRR, 2019)

Learning Symbolic Physics with Graph Networks. - Miles D. Cranmer; Rui Xu; Peter W. Battaglia; Shirley Ho (CoRR, 2019)

Discovering Symbolic Models from Deep Learning with Inductive Biases. - Miles D. Cranmer; Alvaro Sanchez-Gonzalez; Peter W. Battaglia; Rui Xu; Kyle Cranmer; David N. Spergel; Shirley Ho (NeurIPS, 2020)

Unsupervised Resource Allocation with Graph Neural Networks. - Miles D. Cranmer; Peter Melchior; Brian Nord (Preregister@NeurIPS, 2020)

Lagrangian Neural Networks. - Miles D. Cranmer; Sam Greydanus; Stephan Hoyer; Peter W. Battaglia; David N. Spergel; Shirley Ho (CoRR, 2020)

Meta-Learning One-Class Classification with DeepSets: Application in the Milky Way. - Ademola Oladosu; Tony Xu; Philip Ekfeldt; Brian A. Kelly; Miles D. Cranmer; Shirley Ho; Adrian M. Price-Whelan; Gabriella Contardo (CoRR, 2020)

Anomaly Detection for Multivariate Time Series of Exotic Supernovae. - V. Ashley Villar; Miles D. Cranmer; Gabriella Contardo; Shirley Ho; Joshua Yao-Yu Lin (CoRR, 2020)

A Bayesian neural network predicts the dissolution of compact planetary systems. - Miles D. Cranmer; Daniel Tamayo; Hanno Rein; Peter W. Battaglia; Samuel Hadden; Philip J. Armitage; Shirley Ho; David N. Spergel (CoRR, 2021)

A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients. - V. Ashley Villar; Miles D. Cranmer; Edo Berger; Gabriella Contardo; Shirley Ho; Griffin Hosseinzadeh; Joshua Yao-Yu Lin (CoRR, 2021)

Learned Coarse Models for Efficient Turbulence Simulation. - Kimberly L. Stachenfeld; Drummond B. Fielding; Dmitrii Kochkov; Miles D. Cranmer; Tobias Pfaff; Jonathan Godwin; Can Cui; Shirley Ho; Peter W. Battaglia; Alvaro Sanchez-Gonzalez (CoRR, 2021)

Learned Simulators for Turbulence. - Kimberly L. Stachenfeld; Drummond Buschman Fielding; Dmitrii Kochkov; Miles D. Cranmer; Tobias Pfaff; Jonathan Godwin; Can Cui; Shirley Ho; Peter W. Battaglia; Alvaro Sanchez-Gonzalez (ICLR, 2022)

Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter. - Digvijay Wadekar; Leander Thiele; Francisco Villaescusa-Navarro; J. Colin Hill; David N. Spergel; Miles D. Cranmer; Nicholas Battaglia; Daniel Anglés-Alcázar; Lars Hernquist; Shirley Ho (CoRR, 2022)

Automated discovery of interpretable gravitational-wave population models. - Kaze W. K. Wong; Miles D. Cranmer (CoRR, 2022)

The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback. - Digvijay Wadekar; Leander Thiele; J. Colin Hill; Shivam Pandey; Francisco Villaescusa-Navarro; David N. Spergel; Miles D. Cranmer; Daisuke Nagai; Daniel Anglés-Alcázar; Shirley Ho; Lars Hernquist (CoRR, 2022)

Mangrove: Learning Galaxy Properties from Merger Trees. - Christian Kragh Jespersen; Miles D. Cranmer; Peter Melchior; Shirley Ho; Rachel S. Somerville; Austen Gabrielpillai (CoRR, 2022)

A Neural Network Subgrid Model of the Early Stages of Planet Formation. - Thomas Pfeil; Miles D. Cranmer; Shirley Ho; Philip J. Armitage; Tilman Birnstiel; Hubert Klahr (CoRR, 2022)

Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks. - Ji Won Park; Simon Birrer; Madison Ueland; Miles D. Cranmer; Adriano Agnello; Sebastian Wagner-Carena; Philip J. Marshall; Aaron Roodman (CoRR, 2022)

Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study. - David Ruhe; Kaze Wong; Miles D. Cranmer; Patrick Forré (CoRR, 2022)

Learning Integrable Dynamics with Action-Angle Networks. - Ameya Daigavane; Arthur Kosmala; Miles D. Cranmer; Tess E. Smidt; Shirley Ho (CoRR, 2022)

Predicting the thermal Sunyaev-Zel'dovich field using modular and equivariant set-based neural networks. - Leander Thiele; Miles D. Cranmer; William R. Coulton; Shirley Ho; David N. Spergel (Mach. Learn. Sci. Technol., 2022)

Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. - Fabrício Olivetti de França; Marco Virgolin; Michael Kommenda; Maimuna S. Majumder; Miles D. Cranmer; Guilherme Espada; Leon Ingelse; Alcides Fonseca; Mikel Landajuela; Brenden K. Petersen; Ruben Glatt; T. Nathan Mundhenk; et al. (CoRR, 2023)

Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl. - Miles D. Cranmer (CoRR, 2023)

Symbolic Regression on FPGAs for Fast Machine Learning Inference. - Ho Fung Tsoi; Adrian Alan Pol; Vladimir Loncar; Ekaterina Govorkova; Miles D. Cranmer; Sridhara Dasu; Peter Elmer; Philip C. Harris; Isobel Ojalvo; Maurizio Pierini (CoRR, 2023)

Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures. - Christian Pedersen; Tiberiu Tesileanu; Tinghui Wu; Siavash Golkar; Miles D. Cranmer; Zijun Zhang; Shirley Ho (CoRR, 2023)

xVal: A Continuous Number Encoding for Large Language Models. - Siavash Golkar; Mariel Pettee; Michael Eickenberg; Alberto Bietti; Miles D. Cranmer; Géraud Krawezik; François Lanusse; Michael McCabe; Ruben Ohana; Liam Holden Parker; Bruno Régaldo-Saint Blancard; Tiberiu Tesileanu; et al. (CoRR, 2023)

Multiple Physics Pretraining for Physical Surrogate Models. - Michael McCabe; Bruno Régaldo-Saint Blancard; Liam Holden Parker; Ruben Ohana; Miles D. Cranmer; Alberto Bietti; Michael Eickenberg; Siavash Golkar; Géraud Krawezik; François Lanusse; Mariel Pettee; Tiberiu Tesileanu; et al. (CoRR, 2023)

AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models. - François Lanusse; Liam Holden Parker; Siavash Golkar; Miles D. Cranmer; Alberto Bietti; Michael Eickenberg; Géraud Krawezik; Michael McCabe; Ruben Ohana; Mariel Pettee; Bruno Régaldo-Saint Blancard; Tiberiu Tesileanu; et al. (CoRR, 2023)

Robust simulation-based inference in cosmology with Bayesian neural networks. - Pablo Lemos; Miles D. Cranmer; Muntazir Abidi; ChangHoon Hahn; Michael Eickenberg; Elena Massara; David Yallup; Shirley Ho (Mach. Learn. Sci. Technol., 2023)

Rediscovering orbital mechanics with machine learning. - Pablo Lemos; Niall Jeffrey; Miles D. Cranmer; Shirley Ho; Peter W. Battaglia (Mach. Learn. Sci. Technol., 2023)

The Multimodal Universe: Enabling Large-Scale Machine Learning with 100 TB of Astronomical Scientific Data. - Eirini Angeloudi; Jeroen Audenaert; Micah Bowles; Benjamin M. Boyd; David Chemaly; Brian Cherinka; Ioana Ciuca; Miles D. Cranmer; Aaron Do; Matthew Grayling; Erin E. Hayes; Tom Hehir; et al. (NeurIPS, 2024)

Symbolic Regression with a Learned Concept Library. - Arya Grayeli; Atharva Sehgal; Omar Costilla-Reyes; Miles D. Cranmer; Swarat Chaudhuri (NeurIPS, 2024)

Multiple Physics Pretraining for Spatiotemporal Surrogate Models. - Michael McCabe; Bruno Régaldo-Saint Blancard; Liam Holden Parker; Ruben Ohana; Miles D. Cranmer; Alberto Bietti; Michael Eickenberg; Siavash Golkar; Géraud Krawezik; François Lanusse; Mariel Pettee; Tiberiu Tesileanu; et al. (NeurIPS, 2024)

The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning. - Ruben Ohana; Michael McCabe; Lucas Meyer; Rudy Morel; Fruzsina J. Agocs; Miguel Beneitez; Marsha Berger; Blakesley Burkhart; Stuart B. Dalziel; Drummond B. Fielding; Daniel Fortunato; Jared A. Goldberg; et al. (NeurIPS, 2024)

Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task. - Siavash Golkar; Alberto Bietti; Mariel Pettee; Michael Eickenberg; Miles D. Cranmer; Keiya Hirashima; Géraud Krawezik; Nicholas Lourie; Michael McCabe; Rudy Morel; Ruben Ohana; Liam Holden Parker; et al. (CoRR, 2024)

Accelerating Giant Impact Simulations with Machine Learning. - Caleb Lammers; Miles D. Cranmer; Samuel Hadden; Shirley Ho; Norman Murray; Daniel Tamayo (CoRR, 2024)

Machine Learning with Physics Knowledge for Prediction: A Survey. - Joe Watson; Chen Song; Oliver Weeger; Theo Gruner; An Thai Le 001; Kay Hansel; Ahmed Hendawy; Oleg Arenz; Will Trojak; Miles D. Cranmer; Carlo D'Eramo; Fabian Bülow; et al. (Trans. Mach. Learn. Res., 2025)

Call for Action: towards the next generation of symbolic regression benchmark. - Guilherme Seidyo Imai Aldeia; Hengzhe Zhang; Geoffrey F. Bomarito; Miles D. Cranmer; Alcides Fonseca; Bogdan Burlacu; William G. La Cava; Fabrício Olivetti de França (CoRR, 2025)

Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery. - Licong Xu; Milind Sarkar; Anto I. Lonappan; Íñigo Zubeldia; Pablo Villanueva-Domingo; Santiago Casas; Christian Fidler; Chetana Amancharla; Ujjwal Tiwari; Adrian E. Bayer; Chadi Ait Ekioui; Miles D. Cranmer; et al. (CoRR, 2025)

Controllable Patching for Compute-Adaptive Surrogate Modeling of Partial Differential Equations. - Payel Mukhopadhyay; Michael McCabe; Ruben Ohana; Miles D. Cranmer (CoRR, 2025)

SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation. - Fabrício Olivetti de França; Marco Virgolin; Michael Kommenda; Maimuna S. Majumder; Miles D. Cranmer; Guilherme Espada; Leon Ingelse; Alcides Fonseca; Mikel Landajuela; Brenden K. Petersen; Ruben Glatt; T. Nathan Mundhenk; et al. (IEEE Trans. Evol. Comput., 2025)

SymbolFit: Automatic Parametric Modeling with Symbolic Regression. - Ho Fung Tsoi; Dylan S. Rankin; Cecile Caillol; Miles D. Cranmer; Sridhara Dasu; Javier M. Duarte; Philip C. Harris; Elliot Lipeles; Vladimir Loncar (Comput. Softw. Big Sci., 2025)