Publications (67)
Symbolic Regression with a Learned Concept Library
Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes +2
A Neural Network Subgrid Model of the Early Stages of Planet Formation
Thomas Pfeil, Miles Cranmer, Shirley Ho +3
The Denario project: Deep knowledge AI agents for scientific discovery
Francisco Villaescusa-Navarro, Boris Bolliet, Pablo Villanueva-Domingo +33
A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
V. Ashley Villar, Miles Cranmer, Edo Berger +4
GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
Aritra Ghosh, C. Megan Urry, Amrit Rau +11
CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models
Stefano Riva, Carolina Introini, Antonio Cammi +13
A Bayesian neural network predicts the dissolution of compact planetary systems
Miles Cranmer, Daniel Tamayo, Hanno Rein +5
SymTorch: Symbolic Distillation of Neural Networks
Elizabeth S. Z. Tan, Adil Soubki, Miles Cranmer
Comparative biosignatures with systemic retrievals
Tereza Constantinou, Oliver Shorttle, Miles Cranmer +1
Accelerating Giant Impact Simulations with Machine Learning
Caleb Lammers, Miles Cranmer, Sam Hadden +3
AION-1: Omnimodal Foundation Model for Astronomical Sciences
Liam Parker, Francois Lanusse, Jeff Shen +24
Meta-Learning for One-Class Classification with Few Examples using Order-Equivariant Network
Ademola Oladosu, Tony Xu, Philip Ekfeldt +5
Machine Learning with Physics Knowledge for Prediction: A Survey
Joe Watson, Chen Song, Oliver Weeger +12
Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting
Cristiana Diaconu, Jonas Scholz, Aliaksandra Shysheya +4
MIMIC: A Generative Multimodal Foundation Model for Biomolecules
Siavash Golkar, Jake Kovalic, Irina Espejo Morales +28
Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems
Edward T. Stevenson, Eric T. Wolf, Mei Ting Mak +2
Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence
Payel Mukhopadhyay, Stefan S. Nixon, Romain Watteaux +20
Learned Coarse Models for Efficient Turbulence Simulation
Kimberly Stachenfeld, Drummond B. Fielding, Dmitrii Kochkov +7
On the Value of Tokeniser Pretraining in Physics Foundation Models
Hadi Sotoudeh, Payel Mukhopadhyay, Ruben Ohana +4
Predicting the long-term stability of compact multiplanet systems
Daniel Tamayo, Miles Cranmer, Samuel Hadden +11
Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning
Jeff Shen, Francois Lanusse, Liam Holden Parker +24
Automated discovery of interpretable gravitational-wave population models
Kaze W. K Wong, Miles Cranmer
Lagrangian Neural Networks
Miles Cranmer, Sam Greydanus, Stephan Hoyer +3
xVal: A Continuous Numerical Tokenization for Scientific Language Models
Siavash Golkar, Mariel Pettee, Michael Eickenberg +11
Probabilistic Retrofitting of Learned Simulators
Cristiana Diaconu, Miles Cranmer, Richard E. Turner +2
Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms
Philippe Martin Wyder, Judah Goldfeder, Alexey Yermakov +11
Multiple Physics Pretraining for Physical Surrogate Models
Michael McCabe, Bruno Régaldo-Saint Blancard, Liam Holden Parker +11
Predicting the Thermal Sunyaev-Zel'dovich Field using Modular and Equivariant Set-Based Neural Networks
Leander Thiele, Miles Cranmer, William Coulton +2
Rediscovering orbital mechanics with machine learning
Pablo Lemos, Niall Jeffrey, Miles Cranmer +2
Learning Integrable Dynamics with Action-Angle Networks
Ameya Daigavane, Arthur Kosmala, Miles Cranmer +2
Overtone: Cyclic Patch Modulation for Clean, Efficient, and Flexible Physics Emulators
Payel Mukhopadhyay, Michael McCabe, Ruben Ohana +1
$\texttt{Mangrove}$: Learning Galaxy Properties from Merger Trees
Christian Kragh Jespersen, Miles Cranmer, Peter Melchior +3
Normalizing Flows for Hierarchical Bayesian Analysis: A Gravitational Wave Population Study
David Ruhe, Kaze Wong, Miles Cranmer +1
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
Ruben Ohana, Michael McCabe, Lucas Meyer +24
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 +8
Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks
Pablo Lemos, Miles Cranmer, Muntazir Abidi +5
ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets
Edward T. Stevenson, Mei Ting Mak, Eric Wolf +4
Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task
Siavash Golkar, Alberto Bietti, Mariel Pettee +12
Reusability report: Prostate cancer stratification with diverse biologically-informed neural architectures
Christian Pedersen, Tiberiu Tesileanu, Tinghui Wu +4
Stability Constrained Characterization of the 23 Myr-old V1298 Tau System: Do Young Planets Form in Mean Motion Resonance Chains?
Roberto Tejada Arevalo, Daniel Tamayo, Miles Cranmer
Single Frequency CMB Foreground Removal with Inter-scale Machine Learning
Helen Shao, Fiona McCarthy, Blake D. Sherwin +2
Multi-Agent System for Cosmological Parameter Analysis
Andrew Laverick, Kristen Surrao, Inigo Zubeldia +5
Symbolic Regression on FPGAs for Fast Machine Learning Inference
Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar +7
The Seismic Wavefield Common Task Framework
Alexey Yermakov, Yue Zhao, Marine Denolle +13
SymbolFit: Automatic Parametric Modeling with Symbolic Regression
Ho Fung Tsoi, Dylan Rankin, Cecile Caillol +6
Discovering Symbolic Models from Deep Learning with Inductive Biases
Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia +4
Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery
Licong Xu, Milind Sarkar, Anto I. Lonappan +23
Hierarchical Inference of the Lensing Convergence from Photometric Catalogs with Bayesian Graph Neural Networks
Ji Won Park, Simon Birrer, Madison Ueland +6
AstroCLIP: A Cross-Modal Foundation Model for Galaxies
Liam Parker, Francois Lanusse, Siavash Golkar +12
Free-space quantum key distribution to a moving receiver
Jean-Philippe Bourgoin, Brendon L. Higgins, Nick Gigov +5
Charting Galactic Accelerations with Stellar Streams and Machine Learning
Jacob Nibauer, Vasily Belokurov, Miles Cranmer +2
Five parameters are all you need (in $Î$CDM)
Paulo Montero-Camacho, Yin Li, Miles Cranmer
Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme
Rudy Morel, Francesco Pio Ramunno, Jeff Shen +18
HIFlow: Generating Diverse HI Maps and Inferring Cosmology while Marginalizing over Astrophysics using Normalizing Flows
Sultan Hassan, Francisco Villaescusa-Navarro, Benjamin Wandelt +11
Machine Can Automatically Discover Parametric Functions to Model HEP Data
Ho Fung Tsoi, Dylan Rankin, Cecile Caillol +5
Physics Steering: Causal Control of Cross-Domain Concepts in a Physics Foundation Model
Rio Alexa Fear, Payel Mukhopadhyay, Michael McCabe +2
Mitigating radiation damage of single photon detectors for space applications
Elena Anisimova, Brendon L. Higgins, Jean-Philippe Bourgoin +7
Call for Action: towards the next generation of symbolic regression benchmark
Guilherme S. Imai Aldeia, Hengzhe Zhang, Geoffrey Bomarito +5
TNT: Vision Transformer for Turbulence Simulations
Yuchen Dang, Zheyuan Hu, Miles Cranmer +2
The ones that got away: chemical tagging of globular cluster-origin stars with Gaia BP/RP spectra
Sarah G. Kane, Vasily Belokurov, Miles Cranmer +4
Unsupervised Resource Allocation with Graph Neural Networks
Miles Cranmer, Peter Melchior, Brian Nord
Anomaly Detection for Multivariate Time Series of Exotic Supernovae
V. Ashley Villar, Miles Cranmer, Gabriella Contardo +2
A hierarchical Bayesian framework for cosmology using Type 1 AGN variability
Júlia Laguna-Miralles, Vasily Belokurov, Miles Cranmer
Walrus: A Cross-Domain Foundation Model for Continuum Dynamics
Michael McCabe, Payel Mukhopadhyay, Tanya Marwah +22
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
The Multimodal Universe Collaboration, Jeroen Audenaert, Micah Bowles +26
Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter
Digvijay Wadekar, Leander Thiele, Francisco Villaescusa-Navarro +7
Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
Miles Cranmer