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

arXiv: https://arxiv.org/abs/1909.13340 · HTML: https://mlsanity.com/paper/1909.13340/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2019.

## Built on by (most cited first, 7 of 7)

- [Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world simulations](https://mlsanity.com/paper/2102.05527/) (2021)
- [Analyzing the Performance of Deep Encoder-Decoder Networks as Surrogates for a Diffusion Equation](https://mlsanity.com/paper/2302.03786/) (2023)
- [Feasibility Study on Active Learning of Smart Surrogates for Scientific Simulations](https://mlsanity.com/paper/2407.07674/) (2024)
- [AI-coupled HPC Workflows](https://mlsanity.com/paper/2208.11745/) (2022)
- [Surrogate modeling of Cellular-Potts Agent-Based Models as a segmentation task using the U-Net neural network architecture](https://mlsanity.com/paper/2505.00316/) (2025)
- [Scalable Runtime Architecture for Data-driven, Hybrid HPC and ML Workflow Applications](https://mlsanity.com/paper/2503.13343/) (2025)
- [Generative diffusion model surrogates for mechanistic agent-based biological models](https://mlsanity.com/paper/2505.09630/) (2025)

## 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.