NeuralPDE.jl: Automatic Physics-Informed Neural Networks (PINNs)
NeuralPDE.jl is a solver package which consists of neural network solvers for partial differential equations using physics-informed neural networks (PINNs) and the ability to generate neural networks which both approximate physical laws and real data simultaneously.
Features
- Physics-Informed Neural Networks for ODE, SDE, RODE, and PDE solving.
- Ability to define extra loss functions to mix xDE solving with data fitting (scientific machine learning).
- Automated construction of Physics-Informed loss functions from a high-level symbolic interface.
- Sophisticated techniques like quadrature training strategies, adaptive loss functions, and neural adapters to accelerate training.
- Integrated logging suite for handling connections to TensorBoard.
- Handling of (partial) integro-differential equations and various stochastic equations.
- Specialized forms for solving
ODEProblems with neural networks. - Compatibility with Flux.jl and Lux.jl for all the GPU-powered machine learning layers available from those libraries.
- Compatibility with NeuralOperators.jl for mixing DeepONets and other neural operators (Fourier Neural Operators, Graph Neural Operators, etc.) with physics-informed loss functions.
Installation
Assuming that you already have Julia correctly installed, it suffices to import NeuralPDE.jl in the standard way:
import Pkg
Pkg.add("NeuralPDE")Contributing
Please refer to the SciML ColPrac: Contributor's Guide on Collaborative Practices for Community Packages for guidance on PRs, issues, and other matters relating to contributing to SciML.
See the SciML Style Guide for common coding practices and other style decisions.
There are a few community forums:
- The #diffeq-bridged and #sciml-bridged channels in the Julia Slack
- The #diffeq-bridged and #sciml-bridged channels in the Julia Zulip
- On the Julia Discourse forums
- See also SciML Community page
Citation
If you use NeuralPDE.jl in your research, please cite this paper:
@misc{https://doi.org/10.48550/arxiv.2107.09443,
doi = {10.48550/ARXIV.2107.09443},
url = {https://arxiv.org/abs/2107.09443},
author = {Zubov, Kirill and McCarthy, Zoe and Ma, Yingbo and Calisto, Francesco and Pagliarino, Valerio and Azeglio, Simone and Bottero, Luca and Luján, Emmanuel and Sulzer, Valentin and Bharambe, Ashutosh and Vinchhi, Nand and Balakrishnan, Kaushik and Upadhyay, Devesh and Rackauckas, Chris},
keywords = {Mathematical Software (cs.MS), Symbolic Computation (cs.SC), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error Approximations},
publisher = {arXiv},
year = {2021},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}Flux.jl vs Lux.jl
Both Flux and Lux defined neural networks are supported by NeuralPDE.jl. However, Lux.jl neural networks are greatly preferred for many correctness reasons. Particularly, a Flux Chain does not respect Julia's type promotion rules. This causes major problems in that the restructuring of a Flux neural network will not respect the chosen types from the solver. Demonstration:
using Flux, Tracker
x = [0.8; 0.8]
ann = Chain(Dense(2, 10, tanh), Dense(10, 1))
p, re = Flux.destructure(ann)
z = re(Float64(p))While one may think this recreates the neural network to act in Float64 precision, it does not and instead its values will silently downgrade everything to Float32. This is only fixed by Chain(Dense(2, 10, tanh), Dense(10, 1)) |> f64. Similar cases will lead to dropped gradients with complex numbers. This is not an issue with the automatic differentiation library commonly associated with Flux (Zygote.jl) but rather due to choices in the neural network library's decision for how to approach type handling and precision. Thus when using DiffEqFlux.jl with Flux, the user must be very careful to ensure that the precision of the arguments are correct, and anything that requires alternative types (like TrackerAdjoint tracked values, ForwardDiffSensitivity dual numbers, and TaylorDiff.jl differentiation) are suspect.
Lux.jl has none of these issues, is simpler to work with due to the parameters in its function calls being explicit rather than implicit global references, and achieves higher performance. It is built on the same foundations as Flux.jl, such as Zygote and NNLib, and thus it supports the same layers underneath and calls the same kernels. The better performance comes from not having the overhead of restructure required. Thus we highly recommend people use Lux instead and only use the Flux fallbacks for legacy code.
Reproducibility
The documentation of this SciML package was built using these direct dependencies,
Status `~/_work/NeuralPDE.jl/NeuralPDE.jl/docs/Project.toml`
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Info Packages marked with ⌅ have new versions available but compatibility constraints restrict them from upgrading. To see why use `status --outdated`and using this machine and Julia version.
Julia Version 1.12.7
Commit 6d172b025e4 (2026-08-15 08:05 UTC)
Build Info:
Official https://julialang.org release
Platform Info:
OS: Linux (x86_64-linux-gnu)
CPU: 128 × AMD EPYC 9354 32-Core Processor
WORD_SIZE: 64
LLVM: libLLVM-18.1.7 (ORCJIT, znver4)
GC: Built with stock GC
Threads: 1 default, 1 interactive, 1 GC (on 8 virtual cores)
Environment:
JULIA_CPU_THREADS = 8
JULIA_NUM_PRECOMPILE_TASKS = 8
JULIA_DEBUG = DocumenterA more complete overview of all dependencies and their versions is also provided.
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[2702e6a9] EpollShim_jll v0.0.20230411+1
[2e619515] Expat_jll v2.8.4+0
⌅ [b22a6f82] FFMPEG_jll v8.1.2+0
[f5851436] FFTW_jll v3.3.12+0
[a3f928ae] Fontconfig_jll v2.17.1+0
[d7e528f0] FreeType2_jll v2.14.3+1
[559328eb] FriBidi_jll v1.0.17+0
[0656b61e] GLFW_jll v3.5.1+0
[d2c73de3] GR_jll v0.73.27+0
⌅ [b0724c58] GettextRuntime_jll v0.22.4+0
[61579ee1] Ghostscript_jll v9.55.1+0
[020c3dae] Git_LFS_jll v3.7.1+0
[f8c6e375] Git_jll v2.55.0+0
[7746bdde] Glib_jll v2.88.3+0
[3b182d85] Graphite2_jll v1.3.16+0
[2e76f6c2] HarfBuzz_jll v100.14004.0+0
[1d5cc7b8] IntelOpenMP_jll v2025.2.0+0
[aacddb02] JpegTurbo_jll v3.2.0+1
[9c1d0b0a] JuliaNVTXCallbacks_jll v0.2.1+0
[c1c5ebd0] LAME_jll v3.100.3+0
[88015f11] LERC_jll v4.2.0+0
[dad2f222] LLVMExtra_jll v0.0.47+0
[1d63c593] LLVMOpenMP_jll v22.1.7+0
[6206cf0b] LMDB_jll v1.0.0+0
[ad6e5548] LibTracyClient_jll v0.13.1+0
⌅ [e9f186c6] Libffi_jll v3.4.7+0
[7e76a0d4] Libglvnd_jll v1.7.1+1
[94ce4f54] Libiconv_jll v1.18.0+0
[4b2f31a3] Libmount_jll v2.42.0+0
[89763e89] Libtiff_jll v4.7.3+0
[38a345b3] Libuuid_jll v2.42.0+0
[856f044c] MKL_jll v2025.2.0+0
[ef6e0fe3] NVPTX_LLVM_Backend_jll v22.1.7+1
[e98f9f5b] NVTX_jll v3.2.2+0
[e7412a2a] Ogg_jll v1.3.6+0
[9bd350c2] OpenSSH_jll v10.5.1+0
[efe28fd5] OpenSpecFun_jll v0.5.6+0
[91d4177d] Opus_jll v1.6.1+0
[36c8627f] Pango_jll v1.58.2+0
[30392449] Pixman_jll v0.46.4+0
[c0090381] Qt6Base_jll v6.10.2+2
[629bc702] Qt6Declarative_jll v6.10.2+2
[ce943373] Qt6ShaderTools_jll v6.10.2+1
[6de9746b] Qt6Svg_jll v6.10.2+0
[e99dba38] Qt6Wayland_jll v6.10.2+1
[f50d1b31] Rmath_jll v0.5.2+0
[a44049a8] Vulkan_Loader_jll v1.3.243+0
[a2964d1f] Wayland_jll v1.24.0+0
[ffd25f8a] XZ_jll v5.8.3+0
[f67eecfb] Xorg_libICE_jll v1.1.2+0
[c834827a] Xorg_libSM_jll v1.2.6+0
[4f6342f7] Xorg_libX11_jll v1.8.13+0
[0c0b7dd1] Xorg_libXau_jll v1.0.13+0
[935fb764] Xorg_libXcursor_jll v1.2.4+0
[a3789734] Xorg_libXdmcp_jll v1.1.6+0
[1082639a] Xorg_libXext_jll v1.3.8+0
[d091e8ba] Xorg_libXfixes_jll v6.0.2+0
[a51aa0fd] Xorg_libXi_jll v1.8.4+0
[d1454406] Xorg_libXinerama_jll v1.1.7+0
[ec84b674] Xorg_libXrandr_jll v1.5.6+0
[ea2f1a96] Xorg_libXrender_jll v0.9.12+0
[a65dc6b1] Xorg_libpciaccess_jll v0.19.0+0
[c7cfdc94] Xorg_libxcb_jll v1.17.1+0
[cc61e674] Xorg_libxkbfile_jll v1.2.0+0
[e920d4aa] Xorg_xcb_util_cursor_jll v0.1.6+0
[12413925] Xorg_xcb_util_image_jll v0.4.1+0
[2def613f] Xorg_xcb_util_jll v0.4.1+0
[975044d2] Xorg_xcb_util_keysyms_jll v0.4.1+0
[0d47668e] Xorg_xcb_util_renderutil_jll v0.3.10+0
[c22f9ab0] Xorg_xcb_util_wm_jll v0.4.2+0
[35661453] Xorg_xkbcomp_jll v1.4.7+0
[33bec58e] Xorg_xkeyboard_config_jll v2.47.0+2
[c5fb5394] Xorg_xtrans_jll v1.6.0+0
[3161d3a3] Zstd_jll v1.5.7+1
[1e29f10c] demumble_jll v1.3.0+0
[35ca27e7] eudev_jll v3.2.14+0
⌅ [214eeab7] fzf_jll v0.61.1+0
[a4ae2306] libaom_jll v3.14.1+0
[0ac62f75] libass_jll v0.17.5+0
[1183f4f0] libdecor_jll v0.2.2+0
[8e53e030] libdrm_jll v2.4.134+0
[2db6ffa8] libevdev_jll v1.13.4+0
[f638f0a6] libfdk_aac_jll v2.0.4+0
[36db933b] libinput_jll v1.28.1+0
[b53b4c65] libpng_jll v1.6.58+0
[9a156e7d] libva_jll v2.23.0+0
[f27f6e37] libvorbis_jll v1.3.8+0
[009596ad] mtdev_jll v1.1.7+0
[1317d2d5] oneTBB_jll v2022.3.0+0
[8b5cbfcf] tree_sitter_gcn_jll v0.1.0+0
[44208993] tree_sitter_llvm_jll v1.1.0+0
[71e3f6e6] tree_sitter_ptx_jll v0.1.0+0
[f0e86581] tree_sitter_spirv_jll v0.1.0+0
⌅ [1270edf5] x264_jll v10164.0.1+0
[dfaa095f] x265_jll v4.1.0+0
[d8fb68d0] xkbcommon_jll v1.13.0+0
[0dad84c5] ArgTools v1.1.2
[56f22d72] Artifacts v1.11.0
[2a0f44e3] Base64 v1.11.0
[ade2ca70] Dates v1.11.0
[8ba89e20] Distributed v1.11.0
[f43a241f] Downloads v1.7.0
[7b1f6079] FileWatching v1.11.0
[9fa8497b] Future v1.11.0
[b77e0a4c] InteractiveUtils v1.11.0
[ac6e5ff7] JuliaSyntaxHighlighting v1.12.0
[4af54fe1] LazyArtifacts v1.11.0
[b27032c2] LibCURL v0.6.4
[76f85450] LibGit2 v1.11.0
[8f399da3] Libdl v1.11.0
[37e2e46d] LinearAlgebra v1.12.0
[56ddb016] Logging v1.11.0
[d6f4376e] Markdown v1.11.0
[a63ad114] Mmap v1.11.0
[ca575930] NetworkOptions v1.3.0
[44cfe95a] Pkg v1.12.1
[de0858da] Printf v1.11.0
[3fa0cd96] REPL v1.11.0
[9a3f8284] Random v1.11.0
[ea8e919c] SHA v0.7.0
[9e88b42a] Serialization v1.11.0
[1a1011a3] SharedArrays v1.11.0
[6462fe0b] Sockets v1.11.0
[2f01184e] SparseArrays v1.12.0
[f489334b] StyledStrings v1.11.0
[4607b0f0] SuiteSparse
[fa267f1f] TOML v1.0.3
[a4e569a6] Tar v1.10.0
[8dfed614] Test v1.11.0
[cf7118a7] UUIDs v1.11.0
[4ec0a83e] Unicode v1.11.0
[e66e0078] CompilerSupportLibraries_jll v1.3.1+2
[deac9b47] LibCURL_jll v8.15.0+0
[e37daf67] LibGit2_jll v1.9.0+0
[29816b5a] LibSSH2_jll v1.11.3+1
[14a3606d] MozillaCACerts_jll v2025.11.4
[4536629a] OpenBLAS_jll v0.3.29+0
[05823500] OpenLibm_jll v0.8.7+0
[458c3c95] OpenSSL_jll v3.5.6+0
[efcefdf7] PCRE2_jll v10.44.0+1
[bea87d4a] SuiteSparse_jll v7.8.3+2
[83775a58] Zlib_jll v1.3.1+2
[8e850b90] libblastrampoline_jll v5.15.0+0
[8e850ede] nghttp2_jll v1.64.0+1
[3f19e933] p7zip_jll v17.7.0+0
Info Packages marked with ⌃ and ⌅ have new versions available. Those with ⌃ may be upgradable, but those with ⌅ are restricted by compatibility constraints from upgrading. To see why use `status --outdated -m`You can also download the manifest file and the project file.