Cover Image

On the definition and importance of interpretability in scientific machine learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Rowan, Conor, Doostan, Alireza
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Data Analysis, Statistics and Probability History and Philosophy of Physics Physics and Society
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Fermi statistics method applied to model macroscopic demographic data
by: Alberti, Giuseppe
Published: (2022)

Why you do not need to worry about the standard argument that you are a Boltzmann brain
by: Rovelli, Carlo, et al.
Published: (2024)

Algorithmic Randomness, Exchangeability, and the Principal Principle
by: Barrett, Jeffrey A., et al.
Published: (2025)

Maxwell's Demon
by: Kastner, R. E.
Published: (2026)

The Stochastic-Quantum Correspondence
by: Barandes, Jacob A.
Published: (2023)

Revisiting Taylor and the Trinity Test
by: Mone, Elizabeth, et al.
Published: (2024)

Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature
by: Constantin, Andrei, et al.
Published: (2024)

Relational Quantum Dynamics (RQD): An Informational Ontology
by: Zaghi, Arash E.
Published: (2024)

It's all in your head -- fine-tuning arguments do not require aleatoric uncertainty
by: Fowlie, Andrew
Published: (2026)

Real-Time Cascade Mitigation in Power Systems Using Influence Graph Improved by Reinforcement Learning
by: Zhou, Kai, et al.
Published: (2025)

Partial Effective Information Decomposition for Synergistic Causality
by: Yang, Mingzhe, et al.
Published: (2026)

Missing links prediction: comparing machine learning with physics-rooted approaches
by: Santucci, Francesca, et al.
Published: (2026)

Scientific machine learning in Hydrology: a unified perspective
by: Adombi, Adoubi Vincent De Paul
Published: (2025)

Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models
by: Barman, Kristian G., et al.
Published: (2025)

Towards a Large Physics Benchmark
by: Barman, Kristian G., et al.
Published: (2025)

Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification
by: Sharma, Himanshu, et al.
Published: (2024)

Exponentially improved efficient machine learning for quantum many-body states with provable guarantees
by: Che, Yanming, et al.
Published: (2023)

Observing Schrödinger's Cat with Artificial Intelligence: Emergent Classicality from Information Bottleneck
by: Zhang, Zhelun, et al.
Published: (2023)

Physics-guided machine learning predicts the planet-scale performance of solar farms with sparse, heterogeneous, public data
by: Jahangir, Jabir Bin, et al.
Published: (2024)

Human mobility is well described by closed-form gravity-like models learned automatically from data
by: Cabanas-Tirapu, Oriol, et al.
Published: (2023)

Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers
by: Panja, Madhurima, et al.
Published: (2025)

Dissecting the gender divide: Authorship and acknowledgment in scientific publications
by: Kusumegi, Keigo, et al.
Published: (2025)

Understanding complex crowd dynamics with generative neural simulators
by: Minartz, Koen, et al.
Published: (2024)

Reducing hyperparameter sensitivity in measurement-feedback based Ising machines
by: Sevenants, Toon, et al.
Published: (2026)

Errico Presutti 1942-2024
by: Bertini, Lorenzo, et al.
Published: (2025)

Effectiveness of Contact Tracing on Networks with Cliques
by: Rizi, Abbas K., et al.
Published: (2023)

Unsupervised learning of anomalous diffusion data
by: Muñoz-Gil, Gorka, et al.
Published: (2021)

Uncertainty quantification and posterior sampling for network reconstruction
by: Peixoto, Tiago P.
Published: (2025)

Structure and inference in hypergraphs with node attributes
by: Badalyan, Anna, et al.
Published: (2023)

Multi-Scale Node Embeddings for Graph Modeling and Generation
by: Milocco, Riccardo, et al.
Published: (2024)

Robustness of graph embedding methods for community detection
by: Wei, Zhi-Feng, et al.
Published: (2024)

A model for efficient dynamical ranking in networks
by: Della Vecchia, Andrea, et al.
Published: (2023)

Dynamic redundancy and mortality in stochastic search
by: Linn, Samantha, et al.
Published: (2026)

Application of machine learning in grain-related clustering of Laue spots in a polycrystalline energy dispersive Laue pattern
by: Tosson, Amir, et al.
Published: (2024)

Hierarchical geometric deep learning enables scalable analysis of molecular dynamics
by: Pengmei, Zihan, et al.
Published: (2025)

Homophily Within and Across Groups
by: Rizi, Abbas K., et al.
Published: (2024)

Optimal dismantling of directed networks
by: Liu, Xueming, et al.
Published: (2025)

Large scale statistically validated comorbidity networks
by: Crisafulli, Paride, et al.
Published: (2025)

Observing cities as a complex system
by: Prieto-Curiel, Rafael
Published: (2025)

Scaling Pedestrian Crossing Analysis to 100 U.S. Cities via AI-based Segmentation of Satellite Imagery
by: Moran, Marcel, et al.
Published: (2025)