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34 results for “chrisrackauckas”
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@chrisrackauckas The excellent blog post above explains in detail why implicit ODE solvers are considered more robust than explicit ODE solvers (because they do better on linear problems) and why this is NOT true for all problems (roughly speaking, nonlinear problems can behave differently for linear problems; see the blog post for a better explanation which does not fit here).
An extreme example are exponential integrators, which have perfect stability for linear problems (because they use the analytical solution of linear ODEs). Nevertheless, exponential integrators still suffer from stability problems for nonlinear problems.
#NumericalAnalysis #ODEsolver #NumericalIntegration #ExponentialIntegrator
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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!
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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
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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
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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
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How to properly cook your turkey, using agentic AI and #dyad #julialang! Happy Thanksgiving from JuliaHub, hope we can help you with acausal modeling to change your family's holiday!
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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.
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#SciML fact of the day: automatic differentiation fails to give the correct derivative on a lot of very simple functions 😱 😱 😱 . #julialang #automaticdifferentiation
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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_VocLearn more: https://help.juliahub.com/dyad/stable/
#ModelingAndSimulation #AIAgent #JuliaLang #SciML #Dyad #SystemsEngineering #Modelica
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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_VocLearn more: https://help.juliahub.com/dyad/stable/
#ModelingAndSimulation #AIAgent #JuliaLang #SciML #Dyad #SystemsEngineering #Modelica
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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_VocLearn more: https://help.juliahub.com/dyad/stable/
#ModelingAndSimulation #AIAgent #JuliaLang #SciML #Dyad #SystemsEngineering #Modelica
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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_VocLearn more: https://help.juliahub.com/dyad/stable/
#ModelingAndSimulation #AIAgent #JuliaLang #SciML #Dyad #SystemsEngineering #Modelica
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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_VocLearn more: https://help.juliahub.com/dyad/stable/
#ModelingAndSimulation #AIAgent #JuliaLang #SciML #Dyad #SystemsEngineering #Modelica
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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.
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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
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#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!
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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
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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
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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
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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
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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
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Introducing SymbolicSMT.jl for symbolic constraint solving and theorem proving! Built on Z3, test the feasibility of symbolic expressions built using Symbolics.jl. Given Constraints([x > 0, y > 0, x^2 + y^2 <= 1]), ask issatisfiable? isprovable?
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New blog post: Machine learning with hard constraints: Neural Differential-Algebraic Equations (DAEs) as a general formalism.
#sciml #ai4science #hardconstraints #neuralnetworks #dae #acausal #modelingtoolkit #julialang #modelica
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New blog post: Machine learning with hard constraints: Neural Differential-Algebraic Equations (DAEs) as a general formalism.
#sciml #ai4science #hardconstraints #neuralnetworks #dae #acausal #modelingtoolkit #julialang #modelica
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New blog post: Machine learning with hard constraints: Neural Differential-Algebraic Equations (DAEs) as a general formalism.
#sciml #ai4science #hardconstraints #neuralnetworks #dae #acausal #modelingtoolkit #julialang #modelica