Miles D. Cranmer

62 papers A* 6Journal 54Unranked 2

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	On the Value of Tokeniser Pretraining in Physics Foundation Models. CoRR Hadi Sotoudeh, Payel Mukhopadhyay, Ruben Ohana, Michael McCabe, Neil D. Lawrence, Shirley Ho, Miles D. Cranmer
2026	J	`jnl`	Probabilistic Retrofitting of Learned Simulators. CoRR Cristiana Diaconu, Miles D. Cranmer, Richard E. Turner, Tanya Marwah, Payel Mukhopadhyay
2026	J	`jnl`	SymTorch: A Framework for Symbolic Distillation of Deep Neural Networks. CoRR Elizabeth S. Z. Tan, Adil Soubki, Miles D. Cranmer
2025	—	`conf`	Call for Action: towards the next generation of symbolic regression benchmark. GECCO Companion Guilherme Seidyo Imai Aldeia, Hengzhe Zhang, Geoffrey F. Bomarito, Miles D. Cranmer, Alcides Fonseca, Bogdan Burlacu, William G. La Cava, Fabrício Olivetti de França
2025	J	`jnl`	Call for Action: towards the next generation of symbolic regression benchmark. CoRR Guilherme Seidyo Imai Aldeia, Hengzhe Zhang, Geoffrey F. Bomarito, Miles D. Cranmer, Alcides Fonseca, Bogdan Burlacu, William G. La Cava, Fabrício Olivetti de França
2025	J	`jnl`	Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms. CoRR Philippe Martin Wyder, Judah Goldfeder, Alexey Yermakov, Yue Zhao, Stefano Riva, Jan P. Williams, David Zoro, Amy Sara Rude, Matteo Tomasetto, Joe Germany, Joseph Bakarji, Georg Maierhofer, Miles D. Cranmer, J. Nathan Kutz
2025	J	`jnl`	Controllable Patching for Compute-Adaptive Surrogate Modeling of Partial Differential Equations. CoRR Payel Mukhopadhyay, Michael McCabe, Ruben Ohana, Miles D. Cranmer
2025	J	`jnl`	Machine Learning with Physics Knowledge for Prediction: A Survey. Trans. Mach. Learn. Res. Joe Watson, Chen Song, Oliver Weeger, Theo Gruner, An Thai Le, Kay Hansel, Ahmed Hendawy, Oleg Arenz, Will Trojak, Miles D. Cranmer, Carlo D'Eramo, Fabian Bülow, Tanmay Goyal, Jan Peters, Martin W. Hoffmann
2025	J	`jnl`	Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery. CoRR 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, Adrian Dimitrov, James Fergusson, Kahaan Gandhi, Sven Krippendorf, Andrew Laverick, Julien Lesgourgues, Antony Lewis, Thomas Meier, Blake Sherwin, Kristen Surrao, Francisco Villaescusa-Navarro, Chi Wang, Xueqing Xu, Boris Bolliet
2025	J	`jnl`	Physics Steering: Causal Control of Cross-Domain Concepts in a Physics Foundation Model. CoRR Rio Alexa Fear, Payel Mukhopadhyay, Michael McCabe, Alberto Bietti, Miles D. Cranmer
2025	J	`jnl`	Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme. CoRR Rudy Morel, Francesco Pio Ramunno, Jeff Shen, Alberto Bietti, Kyunghyun Cho, Miles D. Cranmer, Siavash Golkar, Olexandr Gugnin, Géraud Krawezik, Tanya Marwah, Michael McCabe, Lucas Meyer, Payel Mukhopadhyay, Ruben Ohana, Liam Holden Parker, Helen Qu, François Rozet, K. D. Leka, François Lanusse, David Fouhey, Shirley Ho
2025	J	`jnl`	SRBench++: Principled Benchmarking of Symbolic Regression With Domain-Expert Interpretation. IEEE Trans. Evol. Comput. 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, Chak Shing Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, Hengzhe Zhang, Grant Dick, Alessandro Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava
2025	J	`jnl`	SymbolFit: Automatic Parametric Modeling with Symbolic Regression. Comput. Softw. Big Sci. Ho Fung Tsoi, Dylan S. Rankin, Cecile Caillol, Miles D. Cranmer, Sridhara Dasu, Javier M. Duarte, Philip C. Harris, Elliot Lipeles, Vladimir Loncar
2025	J	`jnl`	The Denario project: Deep knowledge AI agents for scientific discovery. CoRR Francisco Villaescusa-Navarro, Boris Bolliet, Pablo Villanueva-Domingo, Adrian E. Bayer, Aidan Acquah, Chetana Amancharla, Almog Barzilay-Siegal, Pablo Bermejo, Camille L. Bilodeau, Pablo Cárdenas Ramírez, Miles D. Cranmer, Urbano França, ChangHoon Hahn, Yan-Fei Jiang, Raúl Jiménez, Jun-Young Lee, Antonio Lerario, Osman Mamun, Thomas Meier, Anupam A. Ojha, Pavlos Protopapas, Shimanto Roy, David N. Spergel, Pedro Tarancón-Álvarez, Ujjwal Tiwari, Matteo Viel, Digvijay Wadekar, Chi Wang, Bonny Y. Wang, Licong Xu, Yossi Yovel, Shuwen Yue, Wen-Han Zhou, Qiyao Zhu, Jiajun Zou, Íñigo Zubeldia
2025	J	`jnl`	The Seismic Wavefield Common Task Framework. CoRR Alexey Yermakov, Yue Zhao, Marine Denolle, Yiyu Ni, Philippe Martin Wyder, Judah Goldfeder, Stefano Riva, Jan P. Williams, David Zoro, Amy Sara Rude, Matteo Tomasetto, Joe Germany, Joseph Bakarji, Georg Maierhofer, Miles D. Cranmer, J. Nathan Kutz
2025	J	`jnl`	Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning. CoRR Jeff Shen, François Lanusse, Liam Holden Parker, Ollie Liu, Tom Hehir, Leopoldo Sarra, Lucas Meyer, Micah Bowles, Sebastian Wagner-Carena, Helen Qu, Siavash Golkar, Alberto Bietti, Hatim Bourfoune, Nathan Cassereau, Pierre Cornette, Keiya Hirashima, Géraud Krawezik, Ruben Ohana, Nicholas Lourie, Michael McCabe, Rudy Morel, Payel Mukhopadhyay, Mariel Pettee, Bruno Régaldo-Saint Blancard, Kyunghyun Cho, Miles D. Cranmer, Shirley Ho
2025	J	`jnl`	Walrus: A Cross-Domain Foundation Model for Continuum Dynamics. CoRR Michael McCabe, Payel Mukhopadhyay, Tanya Marwah, Bruno Régaldo-Saint Blancard, François Rozet, Cristiana Diaconu, Lucas Meyer, Kaze W. K. Wong, Hadi Sotoudeh, Alberto Bietti, Irina Espejo, Rio Fear, Siavash Golkar, Tom Hehir, Keiya Hirashima, Géraud Krawezik, François Lanusse, Rudy Morel, Ruben Ohana, Liam Holden Parker, Mariel Pettee, Jeff Shen, Kyunghyun Cho, Miles D. Cranmer, Shirley Ho
2024	J	`jnl`	Accelerating Giant Impact Simulations with Machine Learning. CoRR Caleb Lammers, Miles D. Cranmer, Samuel Hadden, Shirley Ho, Norman Murray, Daniel Tamayo
2024	J	`jnl`	Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task. CoRR 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, Bruno Régaldo-Saint Blancard, Kyunghyun Cho, Shirley Ho
2024	J	`jnl`	Machine Learning with Physics Knowledge for Prediction: A Survey. CoRR Joe Watson, Chen Song, Oliver Weeger, Theo Gruner, An T. Le, Kay Hansel, Ahmed Hendawy, Oleg Arenz, Will Trojak, Miles D. Cranmer, Carlo D'Eramo, Fabian Bülow, Tanmay Goyal, Jan Peters, Martin W. Hoffmann
2024	A*	`conf`	Multiple Physics Pretraining for Spatiotemporal Surrogate Models. NeurIPS 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, Kyunghyun Cho, Shirley Ho
2024	J	`jnl`	SymbolFit: Automatic Parametric Modeling with Symbolic Regression. CoRR Ho Fung Tsoi, Dylan S. Rankin, Cecile Caillol, Miles D. Cranmer, Sridhara Dasu, Javier M. Duarte, Philip C. Harris, Elliot Lipeles, Vladimir Loncar
2024	A*	`conf`	Symbolic Regression with a Learned Concept Library. NeurIPS Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes, Miles D. Cranmer, Swarat Chaudhuri
2024	J	`jnl`	Symbolic Regression with a Learned Concept Library. CoRR Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes, Miles D. Cranmer, Swarat Chaudhuri
2024	A*	`conf`	The Multimodal Universe: Enabling Large-Scale Machine Learning with 100 TB of Astronomical Scientific Data. NeurIPS 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, Shirley Ho, Marc Huertas-Company, Kartheik Iyer, Maja Jablonska, François Lanusse, Henry Leung, Kaisey Mandel, Rafael Martínez-Galarza, Peter Melchior, Lucas Meyer, Liam Holden Parker, Helen Qu, Jeff Shen, Michael J. Smith, Connor Stone, Mike Walmsley, John F. Wu
2024	A*	`conf`	The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning. NeurIPS Ruben Ohana, Michael McCabe, Lucas Meyer, Rudy Morel, Fruzsina Julia Agocs, Miguel Beneitez, Marsha Berger, Blakesley Burkhart, Stuart B. Dalziel, Drummond B. Fielding, Daniel Fortunato, Jared A. Goldberg, Keiya Hirashima, Yan-Fei Jiang, Rich R. Kerswell, Suryanarayana Maddu, Jonah Miller, Payel Mukhopadhyay, Stefan S. Nixon, Jeff Shen, Romain Watteaux, Bruno Régaldo-Saint Blancard, François Rozet, Liam Holden Parker, Miles D. Cranmer, Shirley Ho
2024	J	`jnl`	The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning. CoRR Ruben Ohana, Michael McCabe, Lucas Meyer, Rudy Morel, Fruzsina Julia Agocs, Miguel Beneitez, Marsha Berger, Blakesley Burkhart, Stuart B. Dalziel, Drummond B. Fielding, Daniel Fortunato, Jared A. Goldberg, Keiya Hirashima, Yan-Fei Jiang, Rich R. Kerswell, Suryanarayana Maddu, Jonah Miller, Payel Mukhopadhyay, Stefan S. Nixon, Jeff Shen, Romain Watteaux, Bruno Régaldo-Saint Blancard, François Rozet, Liam Holden Parker, Miles D. Cranmer, Shirley Ho
2023	J	`jnl`	AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models. CoRR 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, Kyunghyun Cho, Shirley Ho
2023	J	`jnl`	Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl. CoRR Miles D. Cranmer
2023	J	`jnl`	Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition. CoRR 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, Chak Shing Lee, Jacob D. Hochhalter, David L. Randall, P. Kamienny, H. Zhang, Grant Dick, Alessandro Simon, Bogdan Burlacu, Jaan Kasak, Meera Vieira Machado, Casper Wilstrup, William G. La Cava
2023	J	`jnl`	Multiple Physics Pretraining for Physical Surrogate Models. CoRR 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, Kyunghyun Cho, Shirley Ho
2023	J	`jnl`	Rediscovering orbital mechanics with machine learning. Mach. Learn. Sci. Technol. Pablo Lemos, Niall Jeffrey, Miles D. Cranmer, Shirley Ho, Peter W. Battaglia
2023	J	`jnl`	Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures. CoRR Christian Pedersen, Tiberiu Tesileanu, Tinghui Wu, Siavash Golkar, Miles D. Cranmer, Zijun Zhang, Shirley Ho
2023	J	`jnl`	Robust simulation-based inference in cosmology with Bayesian neural networks. Mach. Learn. Sci. Technol. Pablo Lemos, Miles D. Cranmer, Muntazir Abidi, ChangHoon Hahn, Michael Eickenberg, Elena Massara, David Yallup, Shirley Ho
2023	J	`jnl`	Symbolic Regression on FPGAs for Fast Machine Learning Inference. CoRR Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar, Ekaterina Govorkova, Miles D. Cranmer, Sridhara Dasu, Peter Elmer, Philip C. Harris, Isobel Ojalvo, Maurizio Pierini
2023	J	`jnl`	xVal: A Continuous Number Encoding for Large Language Models. CoRR 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, Kyunghyun Cho, Shirley Ho
2022	J	`jnl`	A Neural Network Subgrid Model of the Early Stages of Planet Formation. CoRR Thomas Pfeil, Miles D. Cranmer, Shirley Ho, Philip J. Armitage, Tilman Birnstiel, Hubert Klahr
2022	J	`jnl`	Augmenting astrophysical scaling relations with machine learning : application to reducing the SZ flux-mass scatter. CoRR 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
2022	J	`jnl`	Automated discovery of interpretable gravitational-wave population models. CoRR Kaze W. K. Wong, Miles D. Cranmer
2022	J	`jnl`	Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks. CoRR Ji Won Park, Simon Birrer, Madison Ueland, Miles D. Cranmer, Adriano Agnello, Sebastian Wagner-Carena, Philip J. Marshall, Aaron Roodman
2022	A*	`conf`	Learned Simulators for Turbulence. ICLR 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
2022	J	`jnl`	Learning Integrable Dynamics with Action-Angle Networks. CoRR Ameya Daigavane, Arthur Kosmala, Miles D. Cranmer, Tess E. Smidt, Shirley Ho
2022	J	`jnl`	Mangrove: Learning Galaxy Properties from Merger Trees. CoRR Christian Kragh Jespersen, Miles D. Cranmer, Peter Melchior, Shirley Ho, Rachel S. Somerville, Austen Gabrielpillai
2022	J	`jnl`	Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study. CoRR David Ruhe, Kaze Wong, Miles D. Cranmer, Patrick Forré
2022	J	`jnl`	Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks. CoRR Leander Thiele, Miles D. Cranmer, William R. Coulton, Shirley Ho, David N. Spergel
2022	J	`jnl`	Predicting the thermal Sunyaev-Zel'dovich field using modular and equivariant set-based neural networks. Mach. Learn. Sci. Technol. Leander Thiele, Miles D. Cranmer, William R. Coulton, Shirley Ho, David N. Spergel
2022	J	`jnl`	Rediscovering orbital mechanics with machine learning. CoRR Pablo Lemos, Niall Jeffrey, Miles D. Cranmer, Shirley Ho, Peter W. Battaglia
2022	J	`jnl`	Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks. CoRR Pablo Lemos, Miles D. Cranmer, Muntazir Abidi, ChangHoon Hahn, Michael Eickenberg, Elena Massara, David Yallup, Shirley Ho
2022	J	`jnl`	The SZ flux-mass (Y-M) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback. CoRR 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
2021	J	`jnl`	A Bayesian neural network predicts the dissolution of compact planetary systems. CoRR Miles D. Cranmer, Daniel Tamayo, Hanno Rein, Peter W. Battaglia, Samuel Hadden, Philip J. Armitage, Shirley Ho, David N. Spergel
2021	J	`jnl`	A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients. CoRR V. Ashley Villar, Miles D. Cranmer, Edo Berger, Gabriella Contardo, Shirley Ho, Griffin Hosseinzadeh, Joshua Yao-Yu Lin
2021	J	`jnl`	Learned Coarse Models for Efficient Turbulence Simulation. CoRR 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
2021	J	`jnl`	Unsupervised Resource Allocation with Graph Neural Networks. CoRR Miles D. Cranmer, Peter Melchior, Brian Nord
2020	J	`jnl`	Anomaly Detection for Multivariate Time Series of Exotic Supernovae. CoRR V. Ashley Villar, Miles D. Cranmer, Gabriella Contardo, Shirley Ho, Joshua Yao-Yu Lin
2020	A*	`conf`	Discovering Symbolic Models from Deep Learning with Inductive Biases. NeurIPS Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho
2020	J	`jnl`	Discovering Symbolic Models from Deep Learning with Inductive Biases. CoRR Miles D. Cranmer, Alvaro Sanchez-Gonzalez, Peter W. Battaglia, Rui Xu, Kyle Cranmer, David N. Spergel, Shirley Ho
2020	J	`jnl`	Lagrangian Neural Networks. CoRR Miles D. Cranmer, Sam Greydanus, Stephan Hoyer, Peter W. Battaglia, David N. Spergel, Shirley Ho
2020	J	`jnl`	Meta-Learning One-Class Classification with DeepSets: Application in the Milky Way. CoRR Ademola Oladosu, Tony Xu, Philip Ekfeldt, Brian A. Kelly, Miles D. Cranmer, Shirley Ho, Adrian M. Price-Whelan, Gabriella Contardo
2020	—	`conf`	Unsupervised Resource Allocation with Graph Neural Networks. Preregister@NeurIPS Miles D. Cranmer, Peter Melchior, Brian Nord
2019	J	`jnl`	Learning Symbolic Physics with Graph Networks. CoRR Miles D. Cranmer, Rui Xu, Peter W. Battaglia, Shirley Ho
2019	J	`jnl`	Modeling the Gaia Color-Magnitude Diagram with Bayesian Neural Flows to Constrain Distance Estimates. CoRR Miles D. Cranmer, Richard Galvez, Lauren Anderson, David N. Spergel, Shirley Ho
2017	J	`jnl`	Bifrost: a Python/C++ Framework for High-Throughput Stream Processing in Astronomy. CoRR 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, Gregory B. Taylor, Lincoln J. Greenhill