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https://github.com/ovitrac/generativesimulation

This repository is devoted to the emergence of Generative Simulation—a concept that hybridizes large language models (LLMs) and simulation. Simulation engines are developed or wrapped in such a way that they can be directly manipulated by generative AI, without requiring intensive retraining or fine-tuning.
https://github.com/ovitrac/generativesimulation

ai generative-ai llm modeling-and-simulation multiscale-simulation object-oriented-programming python simulation

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This repository is devoted to the emergence of Generative Simulation—a concept that hybridizes large language models (LLMs) and simulation. Simulation engines are developed or wrapped in such a way that they can be directly manipulated by generative AI, without requiring intensive retraining or fine-tuning.

Awesome Lists containing this project

README 

        
# What is Generative simulation?



***Generative Simulation** is an emerging paradigm that develops **advanced numerical simulations using elemental "bricks" accessible to text-generative AI models**. In short, tools like ChatGPT and its consorts excel at programming across many languages and grasping high-level, macroscopic concepts. However, they cannot seamlessly connect physics, chemistry, biology, and mathematics to solve real-world scientific and engineering problems.*



***Generative Simulation** provides modular bricks—representations of physical or conceptual building blocks—that Large Language Models (LLM)  can understand and manipulate to bridge this gap. These bricks can be designed with AI assistance, but the overarching logic, scientific insight, and problem-specific nuance remain in human hands. Once the bricks form a structured language, the subsequent stages of model development, simulation assembly, or code generation can be delegated back to the AI and iteratively refined under human supervision.*



> The bricks provide a clear context that LLM can follow. Simulations and scenarios can be produced from prompts including specific instructions. Clear examples, reusable classes and operators overcome the current limitations of the considered context window size. 



![genrativeSimulation logo](https://raw.githubusercontent.com/ovitrac/generativeSimulation/main/assets/logo.png)



### **Some show-cases**



***



#### Example 1 | **Pizza**³ | A Multiscale Python Toolkit



**Pizza3** is a toolkit designed to simulate the mechanics of soft matter using **LAMMPS** (Large-scale Atomic/Molecular Massively Parallel Simulator), with fully reusable Python objects that AI can understand and extend. This modular approach simplifies the integration of physics across multiple scales, while abstracting away unnecessary details for large-scale, concurrent simulations running in the cloud.



**Computational resources**: +++


**Complexity**: ++ 


**Typical deployment**: HPC, GPU, AWS


**Source**: https://github.com/ovitrac/Pizza3



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#### Example 2 | **SFPPy** | A Python Framework for Food Contact Compliance & Risk Assessment



**SFPPy** is a high-level Python framework that accelerates the evaluation of the safety of materials in contact with food and related products. Based on mass transfer simulations, the methodology is already recognized by authorities in the EU, US, and China. The tool offers a compact syntax to assess the migration of numerous substances in complex scenarios by automatically connecting to databases, computing mass transfer properties from chemical structures, and simulating migration across all layers of a material. The established methodology is further accelerated through AI-enabled scenario generation, simulation execution, and automated report writing within Jupyter Notebooks.



**Computational resources**: +


**Complexity**: +


**Typical deployment**: Standalone, Google Colab, Jupyter Server


**Typical usage**: Notebooks facilitate record keeping, AI assistance, and automatic coding/reporting


**Source**: https://github.com/ovitrac/SFPPy



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#### Example 3 | **SFPPylite** | SFPPy Running in Your Browser Without Assistance



**SFPPylite** is a proof of concept that runs the full **SFPPy** framework directly in a browser using WebAssembly technology, without the need for a server or of installing any software. The service supports collaboration and review by regulatory authorities, making it easier to share and validate compliance results. In other words, the end-user can simply copy/paste from/to his preferred chat-bot and have simulations running without any infrastructure and deep knowledge on modeling and simulation.



**Computational resources**: +


**Complexity**: +


**Typical deployment**: Web browser (Chrome-based/Firefox)


**Typical usage**: Self-learning, classroom training, SME use


**Source**: https://github.com/ovitrac/SFPPylite