123 citations · 359 across the 16 of their papers we have counts for
18 papers
Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter
Zhenyu Dai, Ben Moews, Ricardo Vilalta +1
Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion…
Mapping out the thermodynamic stability of a QCD equation of state with a critical point using active learning
D. Mroczek, M. Hjorth-Jensen, J. Noronha-Hostler +3
The Beam Energy Scan Theory (BEST) collaboration's equation of state (EoS) incorporates a 3D Ising model critical point into the Quantum Chromodynamics (QCD) equation of state from…
A New Constraint on the Nuclear Equation of State from Statistical Distributions of Compact Remnants of Supernovae
Mikhail M. Meskhi, Noah E. Wolfe, Zhenyu Dai +4
Understanding how matter behaves at the highest densities and temperatures is a major open problem in both nuclear physics and relativistic astrophysics. This physics is often enca…
Applications and Techniques for Fast Machine Learning in Science
Allison McCarn Deiana, Nhan Tran, Joshua Agar +84
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time…
The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
T. Aarrestad, M. van Beekveld, M. Bona +36
We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at…
Learning Abstract Task Representations
Mikhail M. Meskhi, Adriano Rivolli, Rafael G. Mantovani +1
A proper form of data characterization can guide the process of learning-algorithm selection and model-performance estimation. The field of meta-learning has provided a rich body o…