Wolfram

What really happens when you click "Simulate" in a Modelica tool like Wolfram System Modeler? This article is the result of what I’ve learned while exploring how Modelica tools process and solve physical models. While the exact implementations differ across tools, the symbolic workflow is fundamentally similar — and understanding it has helped me design better models and troubleshoot more effectively. If you're building or teaching physical system models, or just curious about symbolic simulation workflows, I hope this article helps you too. #Modelica #SystemModeler #SymbolicSimulation #PhysicalModeling #DigitalTwin #EngineeringEducation #WolframLanguage #ControlSystems #Wolfram

Wolfram's guide to function visualization. Symbolic, numeric and geometric algorithms, built in. https://lnkd.in/g7yWGyHv

I’ve just wrapped up a fantastic and impactful research experience with Wolfram (WSS25), where I had the opportunity to work closely with researchers on a project at the intersection of computational modeling, sustainability, and geospatial analysis, all topics that genuinely resonate with me. Working alongside my mentor Philéas Dazeley Gaist, we developed a suite of tools in Wolfram Language to simulate solar shadows and trace celestial paths over space and time. The goal was to better understand how sunlight interacts with the built environment across different locations and times of year. We created two core functions: sunShadowPlot, which visualizes sun-cast shadows on 3D surfaces, and skyPathPlot, which maps the historical and future paths of celestial bodies. These tools were built using AstroPosition, Graphics3D, and ray casting techniques, with potential applications in solar site assessment, daylight modeling, and photovoltaic system design. I’m incredibly grateful to Philéas for being such a patient and dedicated mentor, guiding me from the very first line of code to the final presentation. Whether it was brainstorming the initial idea or helping implement some of the most technically challenging aspects, their support made a huge difference, and I learned a lot through the process. I would also like to thank Christopher Wolfram and John McNally for their thoughtful feedback, guidance, and encouragement, both technical and beyond. One of the highlights was getting to play some guitar with Christopher. Not something I expected in a research setting, but it made the experience even more memorable. And finally, a big thank you to Stephen Wolfram, not just for suggesting the project, but also for taking the time to advise me directly and support the work throughout. If you're interested, here’s a link to our final project: https://lnkd.in/e4agFiyH Excited to carry this experience forward as I keep working toward building cleaner, more equitable energy systems through sustainable technology and design. #Sustainability #CleanEnergy #WolframResearch #GeospatialAnalysis #RenewableEnergy

Over the last weeks, I had the incredible opportunity to participate in the 2025 Wolfram Summer School! I would like to express my sincere gratitude to my post-doc supervisor Prof. Dr. Mariano De Souza from the São Paulo State University (UNESP - Universidade Estadual Paulista "Júlio de Mesquita Filho") for his huge support, as well as for the insightful discussions and valuable proposals regarding the project. I am also thankful to my mentor, Rafael Caballero, for his great support. My special thanks to Stephanie Bowyer for her key-role in helping me secure the full financial support necessary to attend the Wolfram Summer School, and for always being ready to assist me in every possible way. I am also grateful to Daniel Carvalho, Bowen Ping, and Tarang Janawalkar for their valuable assistance with coding-related matters. Last but not least, I extend my deeply appreciation to Stephen Wolfram and the entire Wolfram staff for providing such a transformative, life-changing experience, which I am sure will resonate with me for many years. Also, special thanks to Paul Abbott, Elly Dec, and Christian Pasquel for their time and kindness in recording quick conversations. Check the community post about the developed project in the link: https://lnkd.in/dD3YPhHN

Come to the data side! Learn to use Wolfram Language to extract your data, crunch numbers and uncover unexpected insights in our upcoming webinar "Turning Webpages Into Data with Wolfram Language" July 16 at 9am CT/3pm BST. Register for free here: https://wolfr.am/1wq7r7rxd

Capgemini Engineering came to us with a challenge: make generative AI useful in domains where precision and accountability matter. We built a system that understands natural language, reasons symbolically and returns results engineers can trace and reuse—now imagine what we could do for your organization. https://lnkd.in/gaHdMgmf

Shhh... Breakthroughs in progress. Keep an eye out for this year's Wolfram Summer School student projects. 👀 https://lnkd.in/gAaRRHA4

Bringing computation beyond the classroom! Field trip day with the Wolfram High School Summer Research Program—where students test ideas, models and algorithms beyond the notebook. https://lnkd.in/gHjwf6NX

AI that you can trust: see what's possible when you combine the computational power of Wolfram Language with the domain expertise of Capgemini Engineering. https://lnkd.in/gaHdMgmf

The convergence of scientific computing and engineering is accelerating innovation like never before. That’s why Capgemini is proud to collaborate with Wolfram to create the co-scientist framework - an intelligent assistant designed specifically for the rigor and precision that engineering and scientific domains demand.   At the heart of this collaboration is a shared belief: Generative AI is transformative only when it’s grounded in scientific accuracy, auditability, and domain reasoning.   Unlike conventional language models, the co-scientist computes, verifies, and synthesizes it. Imagine asking it to simulate the thermal behavior of a new material or optimize control logic for a complex mechatronic system and receiving not just suggestions, but precise equations, dynamic models, and verifiable results.   This is Hybrid AI in action; combining the fluency of generative models with Wolfram’s powerful symbolic computation and curated knowledgebase. The result? An AI co-pilot that enables traceable decision-making and unlocks new possibilities across aerospace, automotive, industrial automation, and more.   Together with Wolfram, we’re bridging the gap between natural language interfaces and the exacting world of scientific computing empowering engineers and researchers to design more confidently, innovate more responsibly, and solve the hardest problems faster.   🔗 Learn more about how Capgemini and Wolfram are engineering a better future: https://lnkd.in/gvWEymaf   #HybridAI #AugmentedEngineering #GenerativeAI #EngineeringInnovation #Capgemini #Wolfram