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97 results for “chrisrackauckas”
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@chrisrackauckas #godbolt for #julialang is a thing now?
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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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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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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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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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Interested in scientific machine learning (#sciml)? Physics-informed Neural Networks (#pinn)? Automating the discovery of physical equations with #machinelearning ? Check out this talk which is an introduction to SciML from the viewpoint of applications for Astroinformatics!
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How successful has the SciML Small Grants program been at getting newcomers to contribute to open source software #oss for #sciml #julialang and #ai4science? Very!
* 13 total projects initiated
* ~90% success rateSee the blog post summary of the first year!
https://sciml.ai/news/2025/07/20/sciml_small_grants_year_one_success/
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Our new manuscript shows how to extend automated model discovery and universal differential equations to chaotic systems in #neuroscience using a trick from control literature known as the Prediction Error Method (PEM)!
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Are you in the London area and want to talk about scientific machine learning #sciml and #julialang? Then check out this meetup on April 21st where we will talk about physics-informed neural networks universal differential equations (#ude), and more!
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Interested in the automated discovery of physical equations from data? Check out Julius's talk on DataDrivenDiffeq: a #julialang #sciml #symbolicregression #SymbolicAI package which lets you give time series data and returns the equations which generated the data. It even generates the LaTeX!
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@justinwilkins yup pharmacometrics fosstodon here! #pharmacometrics is generally on Linkedin for some reason. Something about the field likes suits moreso than plain black t-shirts.
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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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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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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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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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Interested in the automated discovery of physical equations from data? Check out Julius's talk on DataDrivenDiffeq: a #julialang #sciml #symbolicregression #SymbolicAI package which lets you give time series data and returns the equations which generated the data. It even generates the LaTeX!
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Interested in the automated discovery of physical equations from data? Check out Julius's talk on DataDrivenDiffeq: a #julialang #sciml #symbolicregression #SymbolicAI package which lets you give time series data and returns the equations which generated the data. It even generates the LaTeX!
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Interested in the automated discovery of physical equations from data? Check out Julius's talk on DataDrivenDiffeq: a #julialang #sciml #symbolicregression #SymbolicAI package which lets you give time series data and returns the equations which generated the data. It even generates the LaTeX!
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Interested in the automated discovery of physical equations from data? Check out Julius's talk on DataDrivenDiffeq: a #julialang #sciml #symbolicregression #SymbolicAI package which lets you give time series data and returns the equations which generated the data. It even generates the LaTeX!
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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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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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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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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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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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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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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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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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How successful has the SciML Small Grants program been at getting newcomers to contribute to open source software #oss for #sciml #julialang and #ai4science? Very!
* 13 total projects initiated
* ~90% success rateSee the blog post summary of the first year!
https://sciml.ai/news/2025/07/20/sciml_small_grants_year_one_success/
