2 citations · 2 across the 8 of their papers we have counts for
10 papers
ASTROLABE: A 3D Particle-Level Surrogate for Cosmology Simulation with Physical Flow Matching
Ibrahim Elsharkawy, David Curtin, Yonatan Kahn +1
The microphysics of dark matter (DM) is imprinted on cosmological structure across an enormous dynamic range. Nonlinear cosmological observables are most reliably modeled with N-bo…
Searching for BSM Experimental Signatures with Large Lagrangian Models
Ibrahim Elsharkawy, Victoria Knapp-Perez, Wahid Bhimji +1
The search for physics Beyond the Standard Model (BSM) is generally limited not by the supply of theory descriptions but by the lack of discriminating experimental observations. A…
Pre-Training for Simulation-Based Science: A Study on Jet Foundation Model Training Objectives
Ibrahim Elsharkawy, Joschka Birk, Vinicius Mikuni +3
Foundation models (FMs) trained on large datasets and fine-tuned on downstream tasks have emerged as a powerful paradigm in AI for science. Industrial FMs are typically trained usi…
Generative models on phase space
Zachary Bogorad, Ibrahim Elsharkawy, Yonatan Kahn +2
Deep generative models such as diffusion and flow matching are powerful machine learning tools capable of learning and sampling from high-dimensional distributions. They are partic…
FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology
Biwei Dai, Po-Wen Chang, Wahid Bhimji +15
Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribution in our universe and allow…
OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers
Ibrahim Elsharkawy, Vinicius Mikuni, Wahid Bhimji +1
We present OmniMol, a state-of-the-art all-to-all transformer-based small molecule machine-learned interatomic potential (MLIP). OmniMol is built by adapting Omnilearned, a foundat…
OmniCosmos: Transferring Particle Physics Knowledge Across the Cosmos
Vinicius Mikuni, Ibrahim Elsharkawy, Benjamin Nachman
Foundation models build an effective representations of data that can be deployed on diverse downstream tasks. Previous research developed the OmniLearned foundation model for coll…
Contrastive Normalizing Flows for Uncertainty-Aware Parameter Estimation
Ibrahim Elsharkawy, Yonatan Kahn
Estimating physical parameters from data is a crucial application of machine learning (ML) in the physical sciences. However, systematic uncertainties, such as detector miscalibrat…
Uncertainty Quantification From Scaling Laws in Deep Neural Networks
Ibrahim Elsharkawy, Yonatan Kahn, Benjamin Hooberman
Quantifying the uncertainty from machine learning analyses is critical to their use in the physical sciences. In this work we focus on uncertainty inherited from the initialization…
FAIR Universe HiggsML Uncertainty Dataset and Competition
Lisa Benato, Wahid Bhimji, Paolo Calafiura +26
The FAIR Universe HiggsML Uncertainty Challenge focused on measuring the physical properties of elementary particles with imperfect simulators. Participants were required to comput…