mlsanityData to 14 Oct 2026

Accelerating scientific discovery with the common task framework

arXiv 2511.04001 ↗First posted 6 Nov 2025cs.LG cs.AI cs.CEcited by 7

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Abstract (arXiv)

Machine learning (ML) and artificial intelligence (AI) algorithms are transforming and empowering the characterization and control of dynamic systems in the engineering, physical, and biological sciences. These emerging modeling paradigms require comparative metrics to evaluate a diverse set of scientific objectives, including forecasting, state reconstruction, generalization, and control, while also considering limited data scenarios and noisy measurements. We introduce a common task framework (CTF) for science and engineering, which features a growing collection of challenge data sets with a diverse set of practical and common objectives. The CTF is a critically enabling technology that has contributed to the rapid advance of ML/AI algorithms in traditional applications such as speech recognition, language processing, and computer vision. There is a critical need for the objective metrics of a CTF to compare the diverse algorithms being rapidly developed and deployed in practice today across science and engineering.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 19 of its 59 references that are in the map. First posted on arXiv 6 Nov 2025.

Builds on

19 of 59 references.

References · 19

  1. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
  2. 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 2,018
  3. 2018Relational inductive biases, deep learning, and graph networkscited by 1,360
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  7. 2020Lagrangian Neural Networkscited by 398
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  11. 2018Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networkscited by 155
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7 more
  1. 2019Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systemscited by 96
  2. 2024Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equationscited by 91
  3. 2021Chaos as an interpretable benchmark for forecasting and data-driven modellingcited by 88
  4. 2023PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEscited by 87
  5. 2022Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal datacited by 37
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  7. 2022The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Controlcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 6.

7 papers.

  1. 2025The Seismic Wavefield Common Task Frameworkcited by 6
  2. 2026Cheap2Rich: A Multi-Fidelity Framework for Data Assimilation and System Identification of Multiscale Physics -- Rotating Detonation Enginescited by 3
  3. 2026SENDAI: A Hierarchical Sparse-measurement, EfficieNt Data AssImilation Frameworkcited by 2
  4. 2026Discovery of Nonlinear Dynamics with Automated Basis Function Generationcited by 2
  5. 2026Data-Driven Reduced-Complexity Modeling of Fluid Flows: A Community Challengecited by 1
  6. 2026UQ-SHRED: uncertainty quantification of shallow recurrent decoder networks for sparse sensing via engressioncited by 1
  7. 2026LAtent Phase Inference from Short time sequences using SHallow REcurrent Decoders (LAPIS-SHRED)cited by 0