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Dr. Chris Rackauckas :julia:<p>How does the mission of JuliaHub and the new Dyad modeling language interact with the <a href="https://fosstodon.org/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a> open source (<a href="https://fosstodon.org/tags/OSS" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>OSS</span></a>) community? In this blog post we go into detail how Dyad is designed to be synergistic with the enabling stronger open source software, giving long-term maintenance structures and show the history of how much of what has evolved in the <a href="https://fosstodon.org/tags/sciml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sciml</span></a> community had been created in tandem with the goals here.</p><p><a href="https://juliahub.com/blog/the-strategic-connection-between-juliahub-dyad-and-the-julia-open-source-community" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">juliahub.com/blog/the-strategi</span><span class="invisible">c-connection-between-juliahub-dyad-and-the-julia-open-source-community</span></a></p>
Dr. Chris Rackauckas :julia:<p>Our new manuscript shows how to extend automated model discovery and universal differential equations to chaotic systems in <a href="https://fosstodon.org/tags/neuroscience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuroscience</span></a> using a trick from control literature known as the Prediction Error Method (PEM)! </p><p><a href="https://arxiv.org/abs/2507.03631" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/abs/2507.03631</span><span class="invisible"></span></a></p><p><a href="https://fosstodon.org/tags/sciml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sciml</span></a> <a href="https://fosstodon.org/tags/ai4science" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ai4science</span></a> <a href="https://fosstodon.org/tags/physicalai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>physicalai</span></a></p>
Dr. Chris Rackauckas :julia:<p>New blog post: Machine learning with hard constraints: Neural Differential-Algebraic Equations (DAEs) as a general formalism.</p><p><a href="https://www.stochasticlifestyle.com/machine-learning-with-hard-constraints-neural-differential-algebraic-equations-daes-as-a-general-formalism/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">stochasticlifestyle.com/machin</span><span class="invisible">e-learning-with-hard-constraints-neural-differential-algebraic-equations-daes-as-a-general-formalism/</span></a></p><p><a href="https://fosstodon.org/tags/sciml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sciml</span></a> <a href="https://fosstodon.org/tags/ai4science" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ai4science</span></a> <a href="https://fosstodon.org/tags/hardconstraints" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>hardconstraints</span></a> <a href="https://fosstodon.org/tags/neuralnetworks" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>neuralnetworks</span></a> <a href="https://fosstodon.org/tags/dae" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>dae</span></a> <a href="https://fosstodon.org/tags/acausal" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>acausal</span></a> <a href="https://fosstodon.org/tags/modelingtoolkit" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelingtoolkit</span></a> <a href="https://fosstodon.org/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a> <a href="https://fosstodon.org/tags/modelica" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>modelica</span></a></p>
Dr. Chris Rackauckas :julia:<p>Earn money working on open source software <a href="https://fosstodon.org/tags/oss" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>oss</span></a>! New project just posted: help make wrappers to connect Symbolics.jl to SymPy. $300 bounty. Information for signing up for the <a href="https://fosstodon.org/tags/SciML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SciML</span></a> small grants program are contained in the link:</p><p><a href="https://sciml.ai/small_grants/#create_wrapper_functions_to_sympy_for_symbolicsjl_300" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">sciml.ai/small_grants/#create_</span><span class="invisible">wrapper_functions_to_sympy_for_symbolicsjl_300</span></a></p><p><a href="https://fosstodon.org/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a> <a href="https://fosstodon.org/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://fosstodon.org/tags/symbolics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>symbolics</span></a> <a href="https://fosstodon.org/tags/sympy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sympy</span></a> <a href="https://fosstodon.org/tags/ode" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ode</span></a></p>
Dr. Chris Rackauckas :julia:<p>New blog post: How chaotic is chaos? How some AI for Science / SciML papers are overstating accuracy claims.</p><p><a href="https://www.stochasticlifestyle.com/how-chaotic-is-chaos-how-some-ai-for-science-sciml-papers-are-overstating-accuracy-claims/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://www.</span><span class="ellipsis">stochasticlifestyle.com/how-ch</span><span class="invisible">aotic-is-chaos-how-some-ai-for-science-sciml-papers-are-overstating-accuracy-claims/</span></a></p><p><a href="https://fosstodon.org/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a> <a href="https://fosstodon.org/tags/sciml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sciml</span></a> <a href="https://fosstodon.org/tags/chaos" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>chaos</span></a> <a href="https://fosstodon.org/tags/ergodic" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ergodic</span></a> <a href="https://fosstodon.org/tags/ai4science" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ai4science</span></a></p>
Ashwin V. MohananA re-introduction
Dr. Chris Rackauckas :julia:<p>If you work in controls, you know: write C code for real-time embedded hardware. You can't use <a href="https://fosstodon.org/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> or <a href="https://fosstodon.org/tags/rstats" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>rstats</span></a> etc. for that, right? With <a href="https://fosstodon.org/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a> v1.12, we demonstrate it's possible to ahead of time compile to small binaries for use in controls applications. <a href="https://fosstodon.org/tags/sciml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sciml</span></a></p><p><a href="https://arxiv.org/abs/2502.01128" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/abs/2502.01128</span><span class="invisible"></span></a></p>
Darren Wilkinson<p>Published in JOSS: 'jax-smfsb: A Python library for stochastic systems biology modelling and inference' <a href="https://doi.org/10.21105/joss.07491" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">doi.org/10.21105/joss.07491</span><span class="invisible"></span></a> <a href="https://mastodon.org.uk/tags/python" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>python</span></a> <a href="https://mastodon.org.uk/tags/JAX" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>JAX</span></a> <a href="https://mastodon.org.uk/tags/scicomp" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>scicomp</span></a> <a href="https://mastodon.org.uk/tags/sciml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sciml</span></a> <a href="https://mastodon.org.uk/tags/sysbio" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sysbio</span></a> <a href="https://mastodon.org.uk/tags/bayesian" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>bayesian</span></a></p>
Dr. Chris Rackauckas :julia:<p>Using higher order automatic differentiation to improve stiff ODE solvers? Using a third order Newton-like method (Halley's) inside the <a href="https://fosstodon.org/tags/sciml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sciml</span></a> <a href="https://fosstodon.org/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a> ODE solvers with Taylor-mode AD, ~25% faster. This shows a path for non-standard automatic differentiation to become standard within numerical algorithms and is an example of symbolic-numeric programming outperforming standard numerical algorithms. See the manuscript for details!</p><p>arxiv.org/abs/2501.16895</p>
Dr. Chris Rackauckas :julia:<p>The problem of building neural surrogates <a href="https://fosstodon.org/tags/sciml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sciml</span></a> 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 <a href="https://fosstodon.org/tags/scitech" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>scitech</span></a> showcasing the advancements of industrialization of <a href="https://fosstodon.org/tags/SciML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SciML</span></a></p><p>Details: arxiv.org/abs/2501.07701</p>
Dr. Chris Rackauckas :julia:<p>New fully adaptive Radau IIA method, achieves state-of-the-art performance for high accuracy on highly stiff ODEs. It has a fully automated order construction with adaptive order, and thus if you use higher precision numbers it can automatically construct 17th, 21st, etc. order versions of the method on the fly. Outperforms the classic Hairer Fortran implementation of radau by about 2x across the board!</p><p>For more details see: <a href="https://arxiv.org/abs/2412.14362" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/abs/2412.14362</span><span class="invisible"></span></a> <a href="https://fosstodon.org/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a> <a href="https://fosstodon.org/tags/sciml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sciml</span></a></p>
Dr. Chris Rackauckas :julia:<p>New version of a very good ODE solver today! IRKGaussLegendre released a SIMD and multithreaded mode. 16th order Implicit Runge-Kutta integrator IRKGL16 for non-stiff symplectic equations which require high accuracy.<br>For more benchmarks, see <a href="https://github.com/SciML/IRKGaussLegendre.jl/blob/master/Benchmarks/NLS-WorkPrecision.ipynb" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/SciML/IRKGaussLegen</span><span class="invisible">dre.jl/blob/master/Benchmarks/NLS-WorkPrecision.ipynb</span></a></p><p><a href="https://fosstodon.org/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a><br> <br><a href="https://fosstodon.org/tags/sciml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sciml</span></a><br> <br><a href="https://fosstodon.org/tags/ode" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>ode</span></a></p>
doctorambient<p><a href="https://mastodon.social/tags/politics" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>politics</span></a> news is getting on top of me today. To calm down I watched some videos explaining how to implement <a href="https://mastodon.social/tags/stochasticdifferentialequations" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>stochasticdifferentialequations</span></a> in <a href="https://mastodon.social/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a>.</p><p>Thanks, <a href="https://mastodon.social/tags/SciML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SciML</span></a> -- I needed a break.</p>
Dr. Chris Rackauckas :julia:<p>@pumas_ai named Best Clinical Pharmacology Technology Firm by the 9th Annual Biotechnology Awards! This demonstrates the power of translating <a href="https://fosstodon.org/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a> <a href="https://fosstodon.org/tags/sciml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sciml</span></a> to industrial practice, building a new foundation of clinical pharmacology.</p>
Dr. Chris Rackauckas :julia:<p>I am very happy to announce the launch of the <a href="https://fosstodon.org/tags/SciML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SciML</span></a> Small Grants program! This is an <a href="https://fosstodon.org/tags/opensource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>opensource</span></a> contributions program to help improve the <a href="https://fosstodon.org/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a> <a href="https://fosstodon.org/tags/sciml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sciml</span></a> organization and some of the issues that have traditionally been overlooked. No numerical/scientific knowledge needed for many of these projects. If you've been looking contribute and needed an impetus to get started, let this be your call to arms!</p><p>For more information, see <a href="https://sciml.ai/small_grants/" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">sciml.ai/small_grants/</span><span class="invisible"></span></a></p>
Dr. Chris Rackauckas :julia:<p>Differentiable Metropolis-Hastings: differentiate through Bayesian estimation to optimize models towards achieving desired probabilistic outcomes, with implementation in <a href="https://fosstodon.org/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a> (<a href="https://fosstodon.org/tags/sciml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sciml</span></a>)</p><p>For more information, see <a href="https://arxiv.org/abs/2306.07961" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/abs/2306.07961</span><span class="invisible"></span></a></p>
Dr. Chris Rackauckas :julia:<p>New structural identifiability analysis features: automatically reparameterize an ODE system to find the best way to make a system easier to learn with <a href="https://fosstodon.org/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a> <a href="https://fosstodon.org/tags/SciML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SciML</span></a> differentiable programming!</p><p>For more, leave a star at <a href="https://github.com/SciML/StructuralIdentifiability.jl" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="ellipsis">github.com/SciML/StructuralIde</span><span class="invisible">ntifiability.jl</span></a> and check out the tutorial</p>
Daniel Lakeland<p>It looks like Optim.jl and IPNewton are probably not really recommended and I should switch to something like Ipopt.jl and OptimizationMOI.jl which I'm trying... <a href="https://mastodon.sdf.org/tags/sciml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sciml</span></a> <a href="https://mastodon.sdf.org/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a></p>
Dr. Chris Rackauckas :julia:<p>Solving f(x)=0 is just Newton's method, right? Well the <a href="https://fosstodon.org/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a> nonlinear solvers have had lots of innovations in this very common numerical problems. Our nonlinear solvers demonstrate robustness where <a href="https://fosstodon.org/tags/SciPy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SciPy</span></a> fails to converge, high performance automated sparsity via integrated compiler tricks, and many more tidbits. Together, this makes NonlinearSolve.jl simple to use yet hit the highest level of performance from naive usage. <a href="https://fosstodon.org/tags/sciml" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>sciml</span></a> </p><p><a href="https://arxiv.org/abs/2403.16341" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">arxiv.org/abs/2403.16341</span><span class="invisible"></span></a></p>
Dr. Chris Rackauckas :julia:<p>Requirements of scientific machine learning (<a href="https://fosstodon.org/tags/SciML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>SciML</span></a>) for surrogates in industry does not match how that academics think! New preprint that describes how SciML metrics need to be reconsidered in the context of industrial requirements. <a href="https://fosstodon.org/tags/julialang" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>julialang</span></a> </p><p><a href="https://osf.io/preprints/osf/p95zn" rel="nofollow noopener" translate="no" target="_blank"><span class="invisible">https://</span><span class="">osf.io/preprints/osf/p95zn</span><span class="invisible"></span></a></p>