mlsanityData to 7 Oct 2026

Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations

arXiv 1909.13340 ↗First posted 29 Sep 2019cs.LG stat.MLcited by 7

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Abstract (arXiv)

We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities. We cover eight patterns for the link of ML to the simulations or systems plus three algorithmic areas: particle dynamics, agent-based models and partial differential equations. The patterns are further divided into three action areas: Improving simulation with Configurations and Integration of Data, Learn Structure, Theory and Model for Simulation, and Learn to make Surrogates.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Sep 2019.

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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1; 2025: 3.

7 papers.

  1. 2021Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world simulationscited by 5
  2. 2023Analyzing the Performance of Deep Encoder-Decoder Networks as Surrogates for a Diffusion Equationcited by 4
  3. 2024Feasibility Study on Active Learning of Smart Surrogates for Scientific Simulationscited by 4
  4. 2022AI-coupled HPC Workflowscited by 2
  5. 2025Surrogate modeling of Cellular-Potts Agent-Based Models as a segmentation task using the U-Net neural network architecturecited by 1
  6. 2025Scalable Runtime Architecture for Data-driven, Hybrid HPC and ML Workflow Applicationscited by 0
  7. 2025Generative diffusion model surrogates for mechanistic agent-based biological modelscited by 0