Join us for a Dyad Modeling Livestream today - this time at 1pm ET / 10 am PT! Michael Tiller will joining us today to model a hybrid-EV powertrain!
Tune in on YouTube and send us your thoughts in the chat!
#SciML
Surrogate accuracy isn’t the same as verification. When you need credibility, analytical/manufactured solutions act like unit tests.
We’ve released an updated preprint (SIGS v3): grammar-valid symbolic candidates → latent-manifold exploration → residual-validated refinement (incl. coupled PDE systems).
For project page click here: https://oroikono.github.io/sigs-paper-site/#benchmarks
#SciML #PDE #NeuroSymbolicAI #eth-ai-center #eth #ai #ml
New fastest explicit non-stiff ODE solver? That's right, we now have something beating the pants off of the high order explicit RK methods! Check out the new symbolic-numeric optimized Taylor methods available in DifferentialEquations.jl! It uses a mix of Taylor-Mode AD, a symbolic post-processing trick, and a new order adaptivity algorithm to give a new level of performance.
See the paper: https://arxiv.org/abs/2602.04086
Your college professor teaches you "A-stable methods are required for stiff ODEs". But PSA, the most commonly used stiff ODE solvers (adaptive order BDF methods) are not A-stable. #sciml #numericalanalysis #diffeq
Physics-Informed Neural Surrogates for Mesh-Invariant Modeling of High-Speed Flows at #AIAA #SciTech!
High-speed flight simulation is computationally brutal. A single CFD run can take hours on a cluster. That's fine for final validation, but not for early design exploration or real-time decision-making.
Neural surrogate that predicts aerodynamic behavior 595x faster than CFD while maintaining ~1% relative error.
Paper: https://lnkd.in/efe2Q_T9
Framework Grokkit đề xuất phương pháp "tính toán thay vì dự đoán" cho AI và KH học. Nó mã hóa toán tử liên tục, cho phép tăng độ phân giải mà không cần huấn luyện lại, giữ sai số cực thấp. Ưu điểm là chạy được trên phần cứng phổ thông vì sự phức tạp nằm ở toán học.
#AI #SciML #MachineLearning #Research #Technology #KHọc #HọcMáy #NghiênCứu #CôngNghệ
https://www.reddit.com/r/LocalLLaMA/comments/1q01el9/do_you_think_this_compute_instead_of_predict/
New paper with J.A. Christen, just accepted in Statistical Methods in Medical Research
"Hazard-based distributional regression via ordinary differential equations"
preprint: http://arxiv.org/abs/2512.16336
R and Julia code + data: https://github.com/FJRubio67/SurvMODE
Scientific machine learning (#SciML) is not just about adding scientific information to machine learning, but it's also about making machine learning accessible to heterogeneous data.
New livestream, #Dyad Modeling Live! In this stream we built up a thermal model of a room using #AgenticAI and added a heat pump with different control strategies and analyzed the power efficiency. Join the fun live next week! #julialang #sciml
ANSYS /Synopsys, one of the largest simulation companies in the world, is partnering with JuliaHub in order to bring #Dyad, #Julialang, and #SciML to next level of adoption. We have many things planned. This is how research becomes reality.
#SciML fact of the day: automatic differentiation fails to give the correct derivative on a lot of very simple functions 😱 😱 😱 . #julialang #automaticdifferentiation
Can Agentic AI turn single purpose code into reusable modular code? Dyad's specialized AI can!
Watch our latest video on AI-assisted model restructuring and physics enhancement:
https://www.youtube.com/watch?v=0RdA-t9_Voc
Learn more: https://help.juliahub.com/dyad/stable/
#ModelingAndSimulation #AIAgent #JuliaLang #SciML #Dyad #SystemsEngineering #Modelica
Check out the latest #Julialang Dispatch Podcast! This episode goes over the changes to the Julia language, its standard libraries, and the main changes in the package ecosystem (#sciml, JuMP, etc.) that occurred over the summer. Most posts coming soon as well!
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.
What is #acausal modeling and how does it lead to better reproducibility and modularity in modeling and simulation? Check out this video which goes step-by-step into building acausal models using the RC circuit and RLC circuit
#LLMs make mistakes. Modeling languages like #Dyad have static analysis to compile-time check whether models are physically possible. What happens when you mix the two in an #agentic workflow? Automated construction of accurate models!
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!
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.
New workshop materials on High-Performance Scientific Modeling with Julia & SciML:
• #Julialang for scientific computing
• ODEs/PDEs & numerical methods
• Symbolic-numeric modeling
• Biological systems (Catalyst.jl)
• Parameter estimation
• #SciML & UDEs
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