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Stochastic Lifestyle

stochasticlifestyle.com

A Random Blog About Math and Life

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’16’17’18’19’20’21’22’23’24’25’26
Posts
97 from 19 Jan 2016 to 12 Feb 2026
Posting
now and then (2 in the last year)
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personal blog
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2026 2

  1. What Agentic AI “Vibe Coding” In The Hands Of Actual Programmers / Engineers
  2. Why Julia’s GPU Accelerated ODE Solvers are 20x-100x Faster than Jax and PyTorch

2025 9

  1. Claude Code sucks but is still useful: experiences maintaining Julia’s SciML scientific computing infrastructure
  2. Implicit ODE Solvers Are Not Universally More Robust than Explicit ODE Solvers, Or Why No ODE Solver is Best
  3. A Guide to Gen AI / LLM Vibecoding for Expert Programmers
  4. Machine learning with hard constraints: Neural Differential-Algebraic Equations (DAEs) as a general formalism
  5. How chaotic is chaos? How some AI for Science / SciML papers are overstating accuracy claims
  6. A Hands on Introduction to Applied Scientific Machine Learning / Physics-Informed Learning
  7. Open Source Component-Based Modeling with ModelingToolkit
  8. The Numerical Analysis of Differentiable Simulation: Automatic Differentiation Can Be Incorrect
  9. JuliaSim: Building a Product which improves Open Source Sustainability

2024 3

  1. Differences Between Methods for Solving Stiff ODEs
  2. Symbolic-Numerics: how compiler smarts can help improve the performance of numerical methods (nonlinear solvers in Julia)
  3. Semantic Versioning (Semver) is flawed, and Downgrade CI is required to fix it

2023 3

  1. ChatGPT performs better on Julia than Python (and R) for Large Language Model (LLM) Code Generation. Why?
  2. DDPS Seminar Talk: Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous models
  3. Summary of Julia Plotting Packages

2022 6

  1. Integrating equation solvers with probabilistic programming through differentiable programming
  2. Direct Automatic Differentiation of (Differential Equation) Solvers vs Analytical Adjoints: Which is Better?
  3. Is Differentiable Programming Actually Necessary? Can’t you just train the neural networks separately?
  4. Accurate and Efficient Physics-Informed Learning Through Differentiable Simulation (ASA Seminar Talk)
  5. Keynote: New Horizons in Modeling and Simulation with Julia (Modelica Conference 2021)
  6. Composing Modeling and Simulation with Julia (2021 Modelica Conference)

2021 9

  1. Engineering Trade-Offs in Automatic Differentiation: from TensorFlow and PyTorch to Jax and Julia
  2. Improved ForwardDiff.jl Stacktraces With Package Tags
  3. The Use and Practice of Scientific Machine Learning
  4. When does the mean and variance define an SDE?
  5. Learning Epidemic Models That Extrapolate, AI4Pandemics
  6. Useful Algorithms That Are Not Optimized By Jax, PyTorch, or Tensorflow
  7. ModelingToolkit, Modelica, and Modia: The Composable Modeling Future in Julia
  8. Generalizing Automatic Differentiation to Automatic Sparsity, Uncertainty, Stability, and Parallelism
  9. JuliaCall Update: Automated Julia Installation for R Packages

2020 7

  1. GPU-Accelerated ODE Solving in R with Julia, the Language of Libraries
  2. COVID-19 Epidemic Mitigation via Scientific Machine Learning (SciML)
  3. Cheap But Effective: Instituting Effective Pandemic Policies Without Knowing Who’s Infected
  4. Glue AD for Full Language Differentiable Programming
  5. Generalized Physics-Informed Learning through Language-Wide Differentiable Programming (Video)
  6. Universal Differential Equations for Scientific Machine Learning (Video)
  7. Scientific Machine Learning: Interpretable Neural Networks That Accurately Extrapolate From Small Data

2019 8

  1. How Inexact Models and Scientific Machine Learning Can Guide Decision Making in Quantitative Systems Pharmacology
  2. Recent advancements in differential equation solver software
  3. A Collection of Jacobian Sparsity Acceleration Tools for Julia
  4. Developing Julia Packages
  5. When do micro-optimizations matter in scientific computing?
  6. The Essential Tools of Scientific Machine Learning (Scientific ML)
  7. Scientific AI: Domain Models with Integrated Machine Learning
  8. Neural Jump SDEs (Jump Diffusions) and Neural PDEs

2018 11

  1. Zero-Cost Abstractions in Julia: Indexing Vectors by Name with LabelledArrays
  2. The Nonlinear Effect of Code Speed on Productivity
  3. Some State of the Art Packages in Julia v1.0
  4. PuMaS.jl: Pharmaceutical Modeling and Simulation Engine
  5. Solving Partial Differential Equations with Julia
  6. Why Numba and Cython are not substitutes for Julia
  7. DifferentialEquations.jl’s Confederated Modular API
  8. Simulation and Control of Biological Stochasticity
  9. Intro to solving differential equations in Julia
  10. Using Juno for Interactive Test-Driven Julia Package Development
  11. Algorithm efficiency comes from problem information

2017 11

  1. Solving Systems of Stochastic PDEs and using GPUs in Julia
  2. Some tips for your Google Summer of Code Application
  3. DifferentialEquations.jl 3.0 and a Roadmap for 4.0
  4. I Like Julia Because It Scales and Is Productive: Some Insights From A Julia Developer
  5. A Comparison Between Differential Equation Solver Suites In MATLAB, R, Julia, Python, C, Mathematica, Maple, and Fortran
  6. Video Introduction to DifferentialEquations.jl
  7. Type-Dispatch Design: Post Object-Oriented Programming for Julia
  8. Video Blog: Developing and Editing Julia Packages
  9. DifferentialEquations.jl 2.0: State of the Ecosystem
  10. Some Fun With Julia Types: Symbolic Expressions in the ODE Solver
  11. Building a Web App in Julia: DifferentialEquations.jl Online

2016 28

  1. 6 Months of DifferentialEquations.jl: Where We Are and Where We Are Going
  2. Modular Algorithms for Scientific Computing in Julia
  3. 7 Julia Gotchas and How to Handle Them
  4. Introducing DifferentialEquations.jl
  5. Using Julia’s Type System For Hidden Performance Gains
  6. Finalizing Your Julia Package: Documentation, Testing, Coverage, and Publishing
  7. Optimal Number of Workers for Parallel Julia
  8. Holding off on Julia for a little bit… you should blog!
  9. Benchmarks of Multidimensional Stack Implementations in Julia
  10. MATLAB 2016a Release Summary for Scientific Computing
  11. Interfacing with a Xeon Phi via Julia
  12. Multiple-GPU Parallelism on the HPC with Julia
  13. Tutorial for Julia on the HPC with GPUs
  14. Multi-node Parallelism in Julia on an HPC (XSEDE Comet)
  15. Blog Upgraded: A Good Experience With Bluehost
  16. Comparison of US HPC Resources
  17. Using Julia’s C Interface to Utilize C Libraries
  18. Setting Variables Element-wise in a Matrix in Mathematica
  19. Simple Parallel Optimization in Mathematica
  20. Why is Mathematica Stopping My Long Calculation?
  21. Julia iFEM3: Solving the Poisson Equation via MATLAB Interfacing
  22. Julia iFEM 2: Optimizing Stiffness Matrix Assembly
  23. Optimizing .*: Details of Vectorization and Metaprogramming
  24. What Exactly is Brownian Motion and Why Does it Matter?
  25. Quick Optimizations in Julia for Performance: A Practical Example
  26. Julia iFEM1: Porting Mesh Generation
  27. WordPress Julia Syntax Highlighting
  28. The New Project