The Haskell Company’s Post

Many of my connections have heard me explain the value of Haskell’s System Analytics team. Check out Zary Peretz latest article on how we leverage our Packaging Line Simulation & Emulation lab on projects. #AskHaskell #PackagingLines #DesignBuild #PackagingEngineering

I recently wrote an article about how the System Analytics team at The Haskell Company uses Simulation and Emulation to quantitatively solve the problems our customers make and help them make data-driven design decisions. Check it out and let me know how you've used simulation or emulation in the past or how you could see using it in the future!

When researchers need to predict the likelihood of natural disasters or other complex scenarios, they build simulations using UQ algorithms. However, those models can vary widely and require enormous computational effort. That’s why we developed UM-Bridge. Its unified interface is accessible from any programming language or framework, and it combines the tools needed to solve complex UQ problems. This makes advanced simulations more accessible and easier to recreate, allowing researchers to effectively account for uncertainty in events ranging from tsunamis to aircraft defects. Learn more about the science behind UM-Bridge here:

🔴 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐓𝐢𝐩❗ 🔸 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗕𝗶𝗴 𝗢 𝗡𝗼𝘁𝗮𝘁𝗶𝗼𝗻 ❓ ⭕ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗕𝗶𝗴 𝗢 𝗡𝗼𝘁𝗮𝘁𝗶𝗼𝗻? Big O Notation, commonly expressed as O(n), indicates the maximum complexity of an algorithm. 𝗜𝘁 𝗱𝗲𝘀𝗰𝗿𝗶𝗯𝗲𝘀 𝗵𝗼𝘄 𝗮𝗻 𝗮𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺’𝘀 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗮𝘀 𝘁𝗵𝗲 𝗶𝗻𝗽𝘂𝘁 𝘀𝗶𝘇𝗲 𝗴𝗿𝗼𝘄𝘀. Here are the key points: 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 𝗼𝗳 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺: Big O measures the efficiency of an algorithm by analyzing its time and space complexity. 𝐓𝐢𝐦𝐞 𝐅𝐚𝐜𝐭𝐨𝐫 𝐨𝐟 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦: It determines the runtime needed to execute an algorithm. However, it does not give a precise time; rather, it concentrates on how the runtime increases with the size of the input. 𝐒𝐩𝐚𝐜𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 𝐨𝐟 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦: Big O also considers memory usage (space complexity) as the input grows. 𝐀𝐥𝐠𝐞𝐛𝐫𝐚𝐢𝐜 𝐑𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: We express Big O using algebraic terms (e.g., O(n), O(n^2), etc.). 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐧𝐠 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: With Big O, we can compare different algorithms and understand how their runtimes differ. For example, an O(n) algorithm will take linear time, while an O(n^2) algorithm will be slower. ❌ 🆁🅴🅼🅴🅼🅱🅴🆁 that Big O is a powerful tool for analyzing and optimizing algorithms, but it won’t give you precise execution times. It’s all about understanding scalability and making informed choices when designing software. 🚀 #programming #datastructure #IT

QuantumPath® offers Q Assets Compositor® for annealing, a graphical interface that avoids the requirement of expanding the squared mathematical expression in order to build the Quadratic Unconstraint Binary Optimization (QUBO) matrix. Programming the annealer basically consists of encoding an interaction matrix whose dimension scales with the number of variables defined to describe our computational problem. But the mathematical expansion of the squared expressions that could appear in the Hamiltonian is not easy to perform. #QuantumPath® simplifies this task by providing a set of abstractions, tools, scalable executions, and unified results that simplify the development stage through a user-friendly graphical interface to build the input data for an annealer device. With #QAssetsCompositor® takes the developer by the hand in the formulation process through forms in which developers must fill in the necessary data for the software solution to be implemented: Parameters, Auxiliary Data, Classes, Variables, Rules. Once the formulation process is completed with Q Assets Compositor® for annealing, QuantumPath® performs the “magic” and generates the Hamiltonian ready to run on quantum computers and simulators. As if that were not enough, all this software technology is agnostic to a specific hardware so, the resulting mathematical formulation can be used, without touching a line of code, on any quantum computer. Try #QuantumPath® and see how you can apply for optimization problems using with #QAssetsCompositor® for annealing, solving problems in the real business. A path to create solutions using quantum computing in the real world https://lnkd.in/dXN_5br9 #quantumprogramming #quantumsoftware #quantumsoftwareengineering #quantumpath #qpath #qdeveloper #qst #QAssetsCompositor #quantumoptimization #quantumannealing #QUBO .

Modeling Talk (Aug 2020) Models, Ptolemy II modeling system, and Beyond Edward Lee, University of California, Berkeley Video Recording: https://lnkd.in/gDvCdf8s Details: https://lnkd.in/g83CidHz Join group for talk announcements: https://lnkd.in/g5ciuNuX Abstract: The Ptolemy Project, which started in 1994, is a large, multifaceted effort focused on modeling, simulation, and design of cyber-physical systems. In this talk, I will describe the principles of the project and its open-source software product, Ptolemy II, with an emphasis on the use of Ptolemy II for modeling and simulation. Ptolemy II is a set of Java packages supporting heterogeneous, concurrent modeling and design. More details: https://lnkd.in/gfP7uRJD Bio: Edward A. Lee has been working on embedded software systems for 40 years. After detours through Yale, MIT, and Bell Labs, landed at Berkeley, where he is now Professor of the Graduate School in EECS. His research is focused on cyber-physical systems. He is the lead author of the open-source software system Ptolemy II, author of leading textbooks on embedded systems and digital communications, and has recently been writing books on philosophical and social implications of technology. His current research is focused on a polyglot coordination language called Lingua Franca that combines the best features of discrete-event modeling, synchronous languages, and actors. More details: https://lnkd.in/ghbuxJMK #modeling #cyberphysicalsystems #simulation #concurrency #pde

**How Flexible Open-Source Libraries Lay Groundwork for Diverse Code Development** One advantage of developing open-source code is the ability to build complexity by leveraging other codes. Below, you'll find a list of library dependencies across various physical domains, each built on top of versatile open-source codes. *ASPECT: A flexible, parallel, and extensible simulator for geodynamics, focusing on the dynamics of the Earth's crust, mantle, and core. *BART: A library aimed at solving problems related to neutron transport and reactor physics, employing advanced numerical methods for accurate simulations. *DFT-FE: A Kohn-Sham density functional theory (DFT) solver that leverages finite element methods for electronic structure calculations, suitable for materials science and chemistry applications. *DOpElib: A Differential Equations and Optimization library designed for solving optimization problems governed by partial differential equations, supporting complex applications in science and engineering. *ExaDG: Specialized in high-order discontinuous Galerkin methods, ExaDG is focused on fluid dynamics and wave propagation problems, optimized for high-performance computing environments. *hyper-deal: An adaptive, high-performance library designed for solving high-dimensional partial differential equations (PDEs), leveraging deal.II for parallel computing capabilities. *Lethe: A versatile library for computational fluid dynamics using the Discrete Element Method (DEM) for multiphase and particle-laden flows, emphasizing user-friendliness and flexibility. *lifex-cfd: A life science-focused CFD library, designed for simulations in biomedical and biological applications, such as blood flow and respiratory airflow. *OpenFCST: An open-source fuel cell simulation toolkit, providing a comprehensive set of tools for modeling and analyzing the performance of fuel cells and electrolyzers. *pi-DoMUS: A library aimed at solving multiphysics problems through distributed, parallel computing, integrating the deal.II library for finite element analysis. *PRISMS: A set of integrated, open-source tools for predicting the structure and properties of materials, focusing on multiscale modeling and simulation techniques.

Objects communicate by messages flowing along connections set up by integrations in Radical Object-Orientation. But how do messages reach the right destination for all objects to cooperate effectively towards a common goal? We have to take a closer look at objects. They cannot stay „polished spheres“ but have to get a structured surface in order to conform to the Principle of Mutual Oblivion (PoMO). Read the article to learn what to put in a message and what rather to put on an object to enable understandable, non-ambiguous data flows. #radicaloo #programming #objectorientation #softwaredevelopment #agility #softwaredesign #softwarearchitecture https://lnkd.in/dkXQhC4r

Excited to announce the release of TORAX, a new open-source tokamak core transport simulator! Building on ideas developed in the fusion community for tokamak scenario modeling, pulse planning, and control, TORAX employs the Python JAX framework to enable more efficient simulations and new powerful workflows. 🚀 Fast: JAX's JIT compilation provides computational efficiency and seamlessly executes on multiple backends including CPU and GPU. 📐 Differentiable: Automatic differentiation, even through complex nonlinear functions like ML-surrogates, allows for gradient-based nonlinear PDE solving, optimization, and sensitivity analysis. 🤖 Seamless ML Integration: JAX's native support for neural network development and inference makes it straightforward to couple TORAX with machine learned surrogates of complex physics models. 📦 Modularity: Python, with its ease-of-use and wide adoption, facilitates coupling within various workflows and to additional physics models. What can you do with TORAX? - Predict scenarios and interpret physics with high throughput. - Explore wide parameter spaces for scenario design and optimization. - Accelerate development of model-based controllers for tokamaks. - Facilitate uncertainty quantification and sensitivity analysis. This is just the beginning for TORAX. The roadmap envisions expanding the physics scope, incorporating a wider range of ML-surrogates and developing application workflows. We believe that through working together with the fusion energy community, we can make TORAX an invaluable tool for accelerating progress. TORAX is open-source and freely available on GitHub ➡ https://lnkd.in/ebAsVXvX Documentation can be found on ReadTheDocs ➡ https://lnkd.in/eGvj8B7t Paper hot off the press on arXiv ➡ arxiv.org/abs/2406.06718