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97 results for “chrisrackauckas”
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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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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
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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.
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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!
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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!
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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.
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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 -
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!
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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! -
SciML Fellowship development 2025 - JumpProcesses.jl: Introducing vr_aggregator for VariableRateJumps, GPU-enhanced SimpleTauLeaping and Extending with τ-Leap Algorithms for Jump Process Simulation
#julialang #ssa #gillespie #sciml #tauleap
https://sciml.ai/news/2025/09/13/jumpprocesses_sciml_fellowship/
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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!
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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
https://www.sciencedirect.com/science/article/pii/S0096300325003649?dgcid=coauthor
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New blog post: Implicit ODE Solvers Are Not Universally More Robust than Explicit ODE Solvers, Or Why No ODE Solver is Best
Talks about how "robust" methods can be less robust in practice. Justifies hundreds of methods in #julialang #sciml
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LinearSolve.jl just got autotuning 🔥 The problem: picking linear solvers is hardware-dependent voodoo. Solution: automatic benchmarking that tells you EXACTLY what to use. Share the results to improve the system!
Details: sciml.ai/news/2025/08/16/linearsolve_autotuning/
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What is the #julialang programming language? This is stitched from the interviews of a lot of developers telling their stories as to why they are committed to the mission of #Julia. Performance, scaling, easy, fun?
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New paper on fitting #neuroscience models to neuroimaging and electrophysiological data! We show that the previous techniques within the field (Spectral DCM) can be greatly improved using automatic differentiation, #julialang #sciml, ModelingToolkit, and more.
This demonstrates how the optimization and Bayesian estimation of the Julia SciML tooling could be the tool analyzing your fMRIs in the near future!
See the link for the full paper: https://direct.mit.edu/imag/article/doi/10.1162/IMAG.a.88/131630/Increasing-spectral-DCM-flexibility-and-speed-by
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The problem of building neural surrogates #sciml for real-world industrial problems is not a problem of choosing neural network architectures, it's a problem of gathering the right training data from the model you're seeking to emulate. We demonstrate this on a turbofan jet engine, achieving 0.1% relative error through an active learning process. This is one of the demonstrations from #scitech showcasing the advancements of industrialization of #SciML
Details: arxiv.org/abs/2501.07701
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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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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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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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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 blog post: How chaotic is chaos? How some AI for Science / SciML papers are overstating accuracy claims.
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New blog post: How chaotic is chaos? How some AI for Science / SciML papers are overstating accuracy claims.
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New blog post: How chaotic is chaos? How some AI for Science / SciML papers are overstating accuracy claims.
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New blog post: How chaotic is chaos? How some AI for Science / SciML papers are overstating accuracy claims.
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New blog post: How chaotic is chaos? How some AI for Science / SciML papers are overstating accuracy claims.
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📣 Calling all #sysbio tool developers and users! Applications for the "Systems biology: community needs, plans, and visions" discussion @JuliaCon are now open! Brainstorm future developments and collaborate with top players in the field.
#SciML #JuliaLang #JuliaCon2023
➡️Apply now: https://forms.gle/icu9Fp1cNPPvCJsW7
➡️More info: https://pretalx.com/juliacon2023/talk/review/TFZC7Z8WHUXFMJSZHET3NKAHB7X88FSF