# GenPhys: From Physical Processes to Generative Models

arXiv: https://arxiv.org/abs/2304.02637 · HTML: https://mlsanity.com/paper/2304.02637/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 18 of its 35 references that are in the map.
- First posted on arXiv 5 Apr 2023.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 18 of 18)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Equivariant Diffusion for Molecule Generation in 3D](https://mlsanity.com/paper/2203.17003/) (2022)
- [LION: Latent Point Diffusion Models for 3D Shape Generation](https://mlsanity.com/paper/2210.06978/) (2022)
- [Generative Modelling With Inverse Heat Dissipation](https://mlsanity.com/paper/2206.13397/) (2022)
- [Unbalanced minibatch Optimal Transport; applications to Domain Adaptation](https://mlsanity.com/paper/2103.03606/) (2021)
- [Why does deep and cheap learning work so well?](https://mlsanity.com/paper/1608.08225/) (2016)
- [Poisson Flow Generative Models](https://mlsanity.com/paper/2209.11178/) (2022)
- [PFGM++: Unlocking the Potential of Physics-Inspired Generative Models](https://mlsanity.com/paper/2302.04265/) (2023)
- [Accelerating Langevin Sampling with Birth-death](https://mlsanity.com/paper/1905.09863/) (2019)
- [Unbalanced Sobolev Descent](https://mlsanity.com/paper/2009.14148/) (2020)

## Built on by (most cited first, 19 of 19)

- [A Survey on Diffusion Models for Time Series and Spatio-Temporal Data](https://mlsanity.com/paper/2404.18886/) (2024)
- [Taming Mode Collapse in Score Distillation for Text-to-3D Generation](https://mlsanity.com/paper/2401.00909/) (2023)
- [QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation](https://mlsanity.com/paper/2406.00132/) (2024)
- [Telegrapher's Generative Model via Kac Flows](https://mlsanity.com/paper/2506.20641/) (2025)
- [A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective](https://mlsanity.com/paper/2312.08248/) (2023)
- [Physics-Inspired Generative Models in Medical Imaging: A Review](https://mlsanity.com/paper/2407.10856/) (2024)
- [Quantum-Inspired Analysis of Neural Network Vulnerabilities: The Role of Conjugate Variables in System Attacks](https://mlsanity.com/paper/2402.10983/) (2024)
- [EquiReg: Equivariance Regularized Diffusion for Inverse Problems](https://mlsanity.com/paper/2505.22973/) (2025)
- [DiFace: Cross-Modal Face Recognition through Controlled Diffusion](https://mlsanity.com/paper/2312.01367/) (2023)
- [MERCURY: A fast and versatile multi-resolution based global emulator of compound climate hazards](https://mlsanity.com/paper/2501.04018/) (2024)
- [Unlocking the Duality between Flow and Field Matching](https://mlsanity.com/paper/2602.02261/) (2026)
- [Augmenting Control over Exploration Space in Molecular Dynamics Simulators to Streamline De Novo Analysis through Generative Control Policies](https://mlsanity.com/paper/2306.14705/) (2023)
- [FreeFlow: A Comprehensive Understanding on Diffusion Probabilistic Models via Optimal Transport](https://mlsanity.com/paper/2312.05486/) (2023)
- [Learning with springs and sticks](https://mlsanity.com/paper/2508.19015/) (2025)
- [Holographic generative flows with AdS/CFT](https://mlsanity.com/paper/2601.22033/) (2026)
- [GenState-AI: State-Aware Dataset for Text-to-Video Retrieval on AI-Generated Videos](https://mlsanity.com/paper/2603.14426/) (2026)
- [LDDMM stochastic interpolants: an application to domain uncertainty quantification in hemodynamics](https://mlsanity.com/paper/2603.28324/) (2026)
- [PFlow-T: A Persistence-Driven Forward Process for Topology-Controlled Generation](https://mlsanity.com/paper/2605.17555/) (2026)
- [Low-Pass Flow Matching](https://mlsanity.com/paper/2606.02177/) (2026)

## Abstract (arXiv)

Since diffusion models (DM) and the more recent Poisson flow generative models (PFGM) are inspired by physical processes, it is reasonable to ask: Can physical processes offer additional new generative models? We show that the answer is yes. We introduce a general family, Generative Models from Physical Processes (GenPhys), where we translate partial differential equations (PDEs) describing physical processes to generative models. We show that generative models can be constructed from s-generative PDEs (s for smooth). GenPhys subsume the two existing generative models (DM and PFGM) and even give rise to new families of generative models, e.g., "Yukawa Generative Models" inspired from weak interactions. On the other hand, some physical processes by default do not belong to the GenPhys family, e.g., the wave equation and the Schr"odinger equation, but could be made into the GenPhys family with some modifications. Our goal with GenPhys is to explore and expand the design space of generative models.