Discriminative versus generative approaches to simulation-based inference
Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to data. This approach is inherently lossy, since histograms are binned and low-dimen...
Saved in:
| Main Authors: | Benjamin Sluijter, Sascha Diefenbacher, Wahid Bhimji, Benjamin Nachman |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2025-01-01
|
| Series: | Machine Learning: Science and Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2632-2153/adf68b |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Bilateral Constraints on Proton Lorentz Violation Effects
by: Ping He, et al.
Published: (2025-01-01) -
Non-Resonant Di-Higgs Searches at the Large Hadron Collider with the CMS Experiment
by: Simona Palluotto
Published: (2025-03-01) -
Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks
by: David Anton, et al.
Published: (2025-05-01) -
Beyond the First Generation of Wind Modeling for Resource Assessment and Siting: From Meteorology to Uncertainty Quantification
by: Mark Kelly
Published: (2025-03-01) -
Evaluating a cassava crop growth model by optimizing genotype-specifc parameters using multienvironment trial breeding data
by: Pamelas M. Okoma, et al.
Published: (2025-06-01)