Hugging Face: https://huggingface.co/flwi/Physics-Foundation-Model
GitHub: https://github.com/FloWsnr/General-Physics-Transformer
Website: https://flowsnr.github.io/blog/physics-foundation-model/
Technical report: https://arxiv.org/abs/2509.13805

Developer: FloWsnr
Released: September 2025
Variants: S, M, L, XL
Architecture: Transformer-based neural differentiator with numerical integration
License: MIT
Modalities: Physical fields in, physical fields out; eight fluid, solid, shock, thermal, and multiphase systems
Runs on: Desktop GPU, NVIDIA only
Formats: PyTorch .pth
On disk: S: 32.0MB / M: 446MB / L: 1.54GB / XL: 3.18GB

Hugging Face: https://huggingface.co/mahindrautela/MORPH
GitHub: https://github.com/lanl/MORPH
Technical report: https://arxiv.org/abs/2509.21670

Developer: Los Alamos National Laboratory
Released: September 2025
Variants: Ti, S, M, L, XL
Architecture: Shape-agnostic vision transformer PDE surrogate with arbitrary data-modality support; full fine-tuning and LoRA adaptation
License: MIT
Modalities: PDE fields in, PDE fields out; mixed scalar and vector fields across 1D, 2D, and 3D systems
Runs on: Desktop GPU, NVIDIA only
Formats: PyTorch .pth
On disk: Ti: 110MB / S: 361MB / M: 1.36GB / L: 5.26GB / XL: 11.8GB

GitHub: https://github.com/functoreality/pdeformer-2
Technical report: https://arxiv.org/abs/2507.15409

Developer: Functoreality
Released: July 2025
Variants: Small, Fast, Base, Base-WDFE
Parameters: Small: 27.75M / Fast: 71.07M / Base: 82.65M / Base-WDFE: 84.70M
Architecture: Computational-graph encoder with a graph Transformer and an implicit neural representation decoder; Base-WDFE uses a weighted DeepSet function encoder for scattered-point inputs
License: Apache 2.0
Modalities: Symbolic 2D PDEs and numeric fields in, 2D solution fields out; arbitrary spatio-temporal query coordinates
Runs on: CPU
Formats: MindSpore checkpoint

Hugging Face: https://huggingface.co/polymathic-ai/walrus
GitHub: https://github.com/PolymathicAI/walrus
Website: https://polymathic-ai.org/blog/walrus/
Technical report: https://arxiv.org/abs/2511.15684

Developer: Polymathic-AI
Released: November 2025
Parameters: 1.3B
Architecture: Transformer-based continuum-dynamics model with compute-adaptive patching, dimensional augmentation, and randomized compression
License: MIT
Modalities: Continuum-dynamics fields in, continuum-dynamics fields out; mixed 2D and 3D physical fields across acoustics, fluids, plasma, active matter, and astrophysics
Runs on: Desktop GPU
Formats: PyTorch, safetensors
On disk: 5.15GB PyTorch / 5.14GB safetensors

Hugging Face: https://huggingface.co/thuerey-group/Tadpole
GitHub: https://github.com/tum-pbs/Tadpole
Website: https://ge.in.tum.de/2026/05/18/tadpole-flexible-scientific-foundation-models/
Technical report: https://arxiv.org/abs/2605.15284

Developer: Thuerey Group, Technical University of Munich
Released: May 2026
Variants: S, B, L; only B weights released
Parameters: S: 8.8M / B: 38.1M / L: 152.1M
Resolution: pre-trained at 64, 128, 256 and 384 cubed, evaluated to 1024 cubed
Architecture: 3D PDE autoencoder pre-trained on single-channel 64x64x64 crops, P3D hybrid backbone with convolutional stages and a transformer bottleneck, adversarial reconstruction loss; latent compression 16 (S) / 8 (B) / 4 (L); Tadpole-DFT adds LoRA, a latent dynamics sub-network, and zero-initialized skip connections for rollout
License: Apache 2.0
Modalities: 3D PDE fields in, 3D PDE fields out
Runs on: Autoencoding: Laptop / Dynamics: Desktop GPU, NVIDIA only
Formats: safetensors, separate encoder and decoder
On disk: B: 60.4MB encoder, 92.6MB decoder

Hugging Face: https://huggingface.co/camlab-ethz/Poseidon-T, https://huggingface.co/camlab-ethz/Poseidon-B, https://huggingface.co/camlab-ethz/Poseidon-L
GitHub: https://github.com/camlab-ethz/poseidon
Website: https://camlab-ethz.github.io/poseidon/
Technical report: https://arxiv.org/abs/2405.19101

Developer: CAMLab, Seminar for Applied Mathematics, ETH Zurich
Released: May 2024
Variants: T, B, L
Parameters: T: 21M / B: 158M / L: 629M
Resolution: 128x128 grid, 4 channels
Architecture: scOT multiscale operator transformer on a SwinV2 backbone, time-conditioned layer norm for continuous-in-time evaluation, 4 hierarchical stages, patch 4, shifted window 16, ConvNeXt residual path; T: embed 48, depths 4/4/4/4 / B: embed 96, depths 8/8/8/8 / L: embed 192, depths 8/8/8/8
License: CC BY-NC 4.0
Modalities: 2D PDE fields in, 2D PDE fields out; density, horizontal velocity, vertical velocity, pressure
Runs on: T/B: Laptop / L: Laptop, 8GB+ memory
Formats: safetensors float32, PyTorch bin
On disk: T: 83.2MB / B: 631.1MB / L: 2.51GB safetensors