Category Simulation & Modeling Projects

Reading Time: 7 minutesReproducibility in chemistry and materials simulation is not just a matter of storing a script, uploading a dataset, or keeping a folder of input files. A workflow is reproducible when another researcher can understand how a scientific question became a model, how that model became an executable run, how the run produced outputs, and how […]

Reading Time: 8 minutesScientific computing is not only about powerful computers or advanced mathematical formulas. It is also about software that helps researchers turn physical questions into models, simulations, and repeatable experiments. In fields such as materials science, chemistry, physics, and engineering, software tools can make it possible to study processes that are difficult, expensive, or slow to […]

Reading Time: 5 minutesModern science is often remembered through a familiar script: a problem becomes visible, an instrument captures something new, and a discovery enters public memory as a breakthrough. That script still matters, but it no longer explains enough. In many fields, the decisive shift did not come from a microscope, a telescope, or a single dramatic […]

Reading Time: 10 minutesPerformance optimization for Python PDE solvers follows a simple rule: profile first, optimize later. Use tools like cProfile and line_profiler to identify actual bottlenecks—typically sparse matrix operations, memory allocation, or algorithmic complexity—before applying targeted fixes. Common gains come from: choosing optimal sparse matrix formats (CSR for reads, CSC for writes), leveraging Numba JIT for tight […]

Reading Time: 7 minutesDevelopers often learn performance from small examples: a faster loop, a cleaner benchmark, a language comparison, a clever micro-optimization. Those examples are useful, but they can also hide the harder truth. Real performance work is rarely about finding one “fast trick.” It is about understanding how a workload behaves when data grows, when memory becomes […]

Reading Time: 10 minutesTL;DR In-situ visualization processes and analyzes simulation data while the simulation runs, directly in memory, instead of writing raw results to disk for later post-processing. This approach is essential for exascale computing where I/O bandwidth cannot keep up with data generation. By integrating visualization during computation, you can: Avoid I/O bottlenecks that cripple traditional workflows […]

Reading Time: 8 minutesLearn how to simulate soft tissue deformation and cardiovascular blood flow using PDEs. Compare FEM vs FVM, implement with FiPy, and apply validation best practices.

Reading Time: 9 minutesReproducible research workflows ensure that simulation results can be exactly recreated by others (or your future self) using the same data, code, and computational environment. Docker provides complete system-level containerization for maximum consistency across platforms, while Conda offers lightweight package and environment management ideal for Python-based scientific computing. For simulation projects, we recommend: (1) use […]

Reading Time: 7 minutesTL;DR Maxwell’s equations describe how electric and magnetic fields evolve and interact. While FiPy was designed for diffusion-type problems, you can simulate electromagnetic waves by treating Maxwell’s equations as a coupled system of transient hyperbolic PDEs. The key is coupling Faraday’s and Ampère’s laws using FiPy’s TransientTerm and custom curl implementations. However, FiPy has limitations […]

Reading Time: 8 minutesMachine learning (ML) surrogates are fast, data-driven approximations of expensive scientific simulations. They enable near real-time predictions, massive design space exploration, and uncertainty quantification that would be impossible with direct simulation alone. Use surrogates when you need iterative optimization or thousands of evaluations; stick with direct simulation for final validation or when high accuracy is […]