6 citations · 14 across the 16 of their papers we have counts for
17 papers
Mesh-Native Tokenisation: Retrofitting Foundation Models to Unstructured Data
Georg Maierhofer, Miles Cranmer, Payel Mukhopadhyay
Physics foundation models are an increasingly popular approach for building versatile emulators for spatiotemporal systems. They require tokenisation layers that lift physical fiel…
Machine Can Automatically Discover Parametric Functions to Model HEP Data
Ho Fung Tsoi, Dylan Rankin, Cecile Caillol +5
In HEP data analyses, finding an adequate function to model binned data has largely relied on a manual process: guess a functional form by intuition, fit, examine, then repeat unti…
Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems
Edward T. Stevenson, Eric T. Wolf, Mei Ting Mak +2
In the sciences, regression tasks often require predicting high-dimensional outputs from few training examples. Multi-output Gaussian processes excel in low-data regimes but typica…
Otter Weather: Skillful and Computationally Efficient Medium-Range Weather Forecasting
Cristiana Diaconu, Jonas Scholz, Aliaksandra Shysheya +4
State-of-the-art medium-range AI weather models can outperform traditional Numerical Weather Prediction (NWP) but require massive training budgets. This restricts usage for under-r…
ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets
Edward T. Stevenson, Mei Ting Mak, Eric Wolf +4
The search for life beyond Earth will depend on detecting faint signatures in the atmospheres of potentially habitable exoplanets. Interpreting those signatures requires understand…
CTF4Nuclear: Common Task Framework for Nuclear Fission and Fusion Models
Stefano Riva, Carolina Introini, Antonio Cammi +13
The demand for clean energy is ever increasing, with new nuclear technologies presenting a complementary solution to renewable energies. However, designing and operating these syst…
MIMIC: A Generative Multimodal Foundation Model for Biomolecules
Siavash Golkar, Jake Kovalic, Irina Espejo Morales +28
Biological function emerges from coupled constraints across sequence, structure, regulation, evolution, and cellular context, yet most foundation models in biology are trained with…
On the Value of Tokeniser Pretraining in Physics Foundation Models
Hadi Sotoudeh, Payel Mukhopadhyay, Ruben Ohana +4
We investigate the impact of tokeniser pretraining on the accuracy and efficiency of physics emulation. Modern high-resolution simulations produce vast volumes of data spanning div…
Probabilistic Retrofitting of Learned Simulators
Cristiana Diaconu, Miles Cranmer, Richard E. Turner +2
Dominant approaches for modelling Partial Differential Equations (PDEs) rely on deterministic predictions, yet many physical systems of interest are inherently chaotic and uncertai…
SymTorch: Symbolic Distillation of Neural Networks
Elizabeth S. Z. Tan, Adil Soubki, Miles Cranmer
What mathematical functions do neural network components learn? Symbolic distillation addresses this question by expressing neural network components with interpretable, closed-for…
Common Task Framework For a Critical Evaluation of Scientific Machine Learning Algorithms
Philippe Martin Wyder, Judah Goldfeder, Alexey Yermakov +11
Machine learning (ML) is transforming modeling and control in the physical, engineering, and biological sciences. However, rapid development has outpaced the creation of standardiz…
The Seismic Wavefield Common Task Framework
Alexey Yermakov, Yue Zhao, Marine Denolle +13
Seismology faces fundamental challenges in state forecasting and reconstruction (e.g., earthquake early warning and ground motion prediction) and managing the parametric variabilit…
The Denario project: Deep knowledge AI agents for scientific discovery
Francisco Villaescusa-Navarro, Boris Bolliet, Pablo Villanueva-Domingo +33
We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the…
Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery
Licong Xu, Milind Sarkar, Anto I. Lonappan +23
We present a multi-agent system for automation of scientific research tasks, cmbagent (https://github.com/CMBAgents/cmbagent). The system is formed by about 30 Large Language Model…
Call for Action: towards the next generation of symbolic regression benchmark
Guilherme S. Imai Aldeia, Hengzhe Zhang, Geoffrey Bomarito +5
Symbolic Regression (SR) is a powerful technique for discovering interpretable mathematical expressions. However, benchmarking SR methods remains challenging due to the diversity o…
SymbolFit: Automatic Parametric Modeling with Symbolic Regression
Ho Fung Tsoi, Dylan Rankin, Cecile Caillol +6
We introduce SymbolFit, a framework that automates parametric modeling by using symbolic regression to perform a machine-search for functions that fit the data while simultaneously…
Symbolic Regression with a Learned Concept Library
Arya Grayeli, Atharva Sehgal, Omar Costilla-Reyes +2
We present a novel method for symbolic regression (SR), the task of searching for compact programmatic hypotheses that best explain a dataset. The problem is commonly solved using…