#sciml search results

Watch Dyad's AI agent build a complete thermal model from just an image! Picture -> validated DAEs in minutes. Features: Auto parameter generation, model optimization, custom animations. All with production-ready Julia code. youtu.be/eKLDVCkJC1s #dyad #julialang #sciml

#Dyad #SciML tutorial! Use Dyad's graphical/textual #acausal system to build models from validated model components and transform into your #digitaltwin! #Dyad = component-based modeling tool (e.g. #Modelica, #Amesim, #Simulink) + AI/ML autocomplete! youtube.com/watch?v=ttQIE3…

Scientific Modeling Cheatsheet: SciML has published a quick reference cheatsheet translating hundreds of functions between Julia, Python and MATLAB. Click here for more. sciml.github.io/Scientific_Mod… #JuliaLang #SciML #Python #MATLAB #ScientificComputing #DataScience #Cheatsheet…

From perception to reasoning—AI is entering a new era. Fujitsu’s #DevSummit keynote explores how #SciML, #AI surrogates & #oneAPI are driving scalable, high-performance, trustworthy AI. Watch now: bit.ly/4qnek5V #UXLFoundation

Newton's method is not efficient for automatic differentiation! Here's a quick explanation as a #sciml fun fact of the day #julialang #differentiableprogramming #automaticdifferentiation

High-Performance Scientific #Modeling with Julia and SciML: This workshop offers in-depth training on advanced scientific computing techniques using the Julia programming language and the SciML (Scientific Machine Learning) ecosystem. github.com/SciML/Julia_Mo… #JuliaLang #SciML…

Excited to share our new research on ASNO—Adaptive Scientific Neural Operator! 📄 We've designed a novel model for efficient scientific machine learning. Check out the paper: iopscience.iop.org/article/10.108… #ML #AI #SciML #NeuralOperator

SciML Developer Chat Episode 1: Base Splits and Symbolics Precompilation Welcome to the first episode of the SciML Dev Chat! We discuss the latest developments in the #SciML (Scientific Machine Learning) ecosystem for #julialang! youtu.be/0yQ4aZ-ABhY

MIT Julia Lab: looking for postdoctoral researchers in #julialang open source software development, scientific machine learning (#SciML), and systems biological / pharmacological modeling (#QSP) for research in equation discovery for large stiff systems. julia.mit.edu/projects/#post…

Claude code sucks... but it's still very useful! Check out this blog post that details how Claude is deployed to solve problems in the #Julialang #SciML open source scientific computing infrastructure. stochasticlifestyle.com/claude-code-in… #llm #vibecoding #physics #opensource #oss

Very excited to share our work on "Science in the Age of Foundation Models", where we discuss applications of foundation models in time series forecasting and PDEs including weather forecasting and aerodynamics. #sciml #pdes #cfd #amazonscience

New workshop materials on High-Performance Scientific Modeling with Julia & SciML: • Julia for scientific computing • ODEs/PDEs & numerical methods • Biological systems (Catalyst.jl) • Parameter estimation • Scientific ML & UDEs github.com/SciML/Julia_Mo… #JuliaLang #SciML

📢 Calling for (4-page) workshop papers on #differentiable programming and #SciML. This is a great opportunity to showcase your work or work in progress alongside keynote presentations by Petros Koumoutsakos, @PatrickKidger, @thuereyGroup & Astrid Walle.

DifferentialEquations.jl is many things, and lots of people only use a small portion of it. Check out the JuliaCon 2025 workshop: introduces many aspects of the packages that the developers feel are underutilized and under-understood! #julialang #sciml youtube.com/watch?v=lSGFAm…

Check out the JuliaCon workshop SciML in Fluid Dynamics (CFD): Surrogates of Weather Models. Go into depth on all of the major surrogate architectures and give them a try on the weather model challenge problem! youtube.com/watch?v=PfRxU2… #julialang #juliacon #sciml

Sundials.jl v5.0: Update to SUNDIALS v7 and Improved DAE Initialization A major update that brings significant improvements to differential-algebraic equation (DAE) solving and upgrades to the latest Sundials C library sciml.ai/news/2025/09/1… #julialang #sciml #sundials #dae

Mixed precision and automatic GPU offloading for Newton solves and stiff ODE solvers has now landed thanks to improvements in LinearSolve.jl. Run LinearSolveAutotune.jl so that things like usage of GPUs in #julialang #sciml becomes automatic! sciml.ai/news/2025/09/0…

New Symbolic-Numeric algorithm which uses rational polynomial interpolation mixed with differential algebra in order to give a highly robust method for parameter estimation and solving inverse problems on ODEs. #julialang #sciml #symbolicnumeric #ode sciencedirect.com/science/articl…

Solution of nonlinear PDEs is not just a simple interpolation (linear combination) of solutions of the same PDE for different parameters, BCs and ICs. Even for linear PDEs it does not work as soon as we change the domain and BCs. This is what #SciML people don't understand.

Complex system modeling often requires stitching together fragmented tools. #Dyad, powered by Julia and #SciML, changes this by combining symbolic #modeling, composable libraries, and packages like SatelliteToolbox.jl into a single workflow. From orbital mechanics to power…

Scientific Modeling Cheatsheet: SciML has published a quick reference cheatsheet translating hundreds of functions between Julia, Python and MATLAB. Click here for more. sciml.github.io/Scientific_Mod… #JuliaLang #SciML #Python #MATLAB #ScientificComputing #DataScience #Cheatsheet…

#neuralnetworks #PINNs #SciML

#SciML #AI4Science #FEM #MachineLearning

Learning Building Thermal Dynamics using Neural State Space Models in NeuroMANCER. Colab example: colab.research.google.com/github/pnnl/ne… Paper: sciencedirect.com/science/articl… Neuromancer SciML library: github.com/pnnl/neuromanc… #SciML #PIML #NSSM #dynamics #themalsystem #building #energy #openrouce

Hello #FAU. Thanks for the quick plan to host me and letting me present our exciting mathematics of ML in infinite-dimensions, #operatorlearning. #sciML Their "Pattern Recognition Laboratory" is completing 50 years! 💥

Learning Stable Deep Koopman Operators in Neuromancer. Colab: colab.research.google.com/github/pnnl/ne… Neuromancer library: github.com/pnnl/neuromanc… #Pytorch #SciML #Koopman #PNNL #opensource #dynamicalsystems

Quoted in @Nature article "Is AI leading to a reproducibility crisis in science?" by @philipcball, I may sound a bit harsh, but it's the truth… #SciML #reproducibility

I'm honored and excited to join the SIAM Journal on Scientific Computing (SISC) as an associate editor for the new Scientific Machine Learning section starting in 2024. If you're working on exciting SciML research, consider submitting your best work to SISC! #SciML

Got a new code review buddy! #julialang #sciml #meow

Physics simulations? Yes, there's an AI for that. theresanaiforthat.com/ai/siml-ai/ #PhysicsML #SciML

Fantastic ⁦@ChrisRackauckas⁩ talk at ⁦@JuliaConOrg⁩ about #SciML 😌

Our @stetuned and @fr_regazzoni co-organized with @rozzagroup the Workshop 💡Scientific Machine Learning, emerging topics💡 held at @Sissaschool, #Trieste #SMLET #SciML @mox_lab #DaVittorio

What I have been saying ever since this craze started! Quite a comprehensive study of biases and issues with all things #SciML for CFD. These findings equally apply to other domains. Full paper at arxiv.org/abs/2407.07218 #AI #ML #NeuralNetworks #PINNs #PIML #PEML #PGML

@JuliaConOrg was hectic and amazing. Thank you everyone for the conversations and support. You can see how frantic it was from the wear and tear on the badge, falling off by the end. So many #julialang #sciml workshops and breakouts. I cannot wait for the next one!

Out today: using social media data in neural ODEs to forecast COVID-19 outbreaks. All done with #julialang #sciml of course. nature.com/articles/s4159…

Differentiable Metropolis-Hastings: differentiate through Bayesian estimation to optimize models towards achieving desired probabilistic outcomes, with implementation in #julialang (#sciml) For more information, see arxiv.org/abs/2306.07961

Scientific Machine Learning (#SciML) is an emerging field that synergistically blends machine learning and scientific computing based on mechanistic models, usually yielding increased interpretability, performance and data efficiency. (2/11) Image from: frontiersin.org/articles/10.33…

📣New paper alert: "Verification and Validation for Trustworthy Scientific Machine Learning" We propose 16 recommendations to establish trust in #SciML models through rigorous V&V practices adapted from computational science—link in reply #MachineLearning #ComputationalScience

I'm thrilled to share that I've started a Postdoc position @Penn! 🎉🎊 I'll be working with Nat Trask on scientific deep learning and structure-preserving algorithms. Looking forward to learning and contributing to this amazing field with new ideas! #AI4Science #SciML #UPennMEAM

#julialang #sciml for perfume engineering. Mixing physical models with machine learned quantities in order to predict and classify odors. chemrxiv.org/engage/chemrxi…