Todd Mostak

🚀 𝗖𝗙𝗗 𝗯𝗲𝘆𝗼𝗻𝗱 𝗺𝗲𝘀𝗵𝗲𝘀: 𝗧𝗨 𝗗𝗮𝗿𝗺𝘀𝘁𝗮𝗱𝘁 𝗮𝗻𝗱 𝗜𝗔𝗡𝗨𝗦 𝗦𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗴𝗼 𝗺𝗲𝘀𝗵𝗹𝗲𝘀𝘀! As part of the #NHR4CES initiative (National High Performance Computing for Computational Engineering Science), a remarkable milestone has been achieved in the 𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗳 𝗖𝗙𝗗 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀. M. Elwardi Fadeli has introduced a 𝗻𝗼𝘃𝗲𝗹 𝗰𝗼𝗱𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 that redefines how we approach 𝗺𝗲𝘀𝗵𝗹𝗲𝘀𝘀 𝗺𝗲𝘁𝗵𝗼𝗱𝘀. His work - presented at the Math-to-Product Conference 2025 in Valencia - lays the groundwork for a 𝗻𝗲𝘄 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗵𝘆𝗯𝗿𝗶𝗱, 𝗮𝗻𝗱 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲-𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺𝘀. At the heart of this framework is a 𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁-𝘁𝗼-𝗰𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹 which has been inspired by the Language Server Protocol in modern IDEs. This design paradigm enables: ➡️ Seamless hybridization of meshless and mesh-based solvers ➡️ Dynamic negotiation of capabilities between independently developed components ➡️ Rapid prototyping across diverse numerical strategies From strong-form MWR and SPH to Kansa’s RBF collocation and generalized moving least squares (GMLS) - the 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝘂𝗮𝗹𝗹𝘆 𝗰𝗮𝗽𝘁𝘂𝗿𝗲𝘀 𝗮 𝘄𝗶𝗱𝗲 𝘀𝗽𝗲𝗰𝘁𝗿𝘂𝗺 𝗼𝗳 𝘁𝗿𝘂𝗹𝘆 𝗺𝗲𝘀𝗵-𝗳𝗿𝗲𝗲 𝗺𝗲𝘁𝗵𝗼𝗱𝘀. 👉 If you care about 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗵𝗶𝗴𝗵-𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀, 𝗺𝗲𝘀𝗵𝗹𝗲𝘀𝘀 𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗶𝗻𝘁𝗲𝗿𝗼𝗽𝗲𝗿𝗮𝗯𝗹𝗲 𝘀𝗼𝗹𝘃𝗲𝗿𝘀, this is one to watch out for! 📝 Presentation slides: https://lnkd.in/eC_eXnn6 𝗘𝗻𝗷𝗼𝘆! #OpenSource #OpenFOAM #CFD #Technology #Meshless #HPC #SimulationEngineering #OpenScience #SPH #RBF #GMLS #NHR4CES #ModularSoftware #StrongFormCFD #ComponentArchitecture #TUDarmstadt #IANUS #IANUSSimulation IANUS Simulation GmbH Technische Universität Darmstadt

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Excited for this session at #NVIDIAGTC 2026 👇 Leo Guo (GM, Foxconn) will share how Foxconn is leveraging digital twins built with NVIDIA Omniverse to design, monitor, and scale AI factories across the globe. A great look at how simulation and real-world operations are coming together. 📆 March 16 | 4:00 p.m. PT 🔗 https://bit.ly/46glLUp

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Bigger datasets aren’t always better MIT LIDS researchers have developed a new way to pinpoint exactly how much data is needed to solve complex problems. LIDS PhD student Omar Bennouna, former MIT postdoc Mohammed Amine Bennouna, and LIDS PIs Saurabh Amin and Asu Ozdaglar have introduced an algorithmic method that provably identifies the smallest dataset required to guarantee an optimal solution—often using far fewer measurements than conventional approaches assume. Their mathematical framework applies broadly to structured decision-making under uncertainty, from supply chain management to electricity network optimization. “We’ve shown that with careful selection, you can guarantee optimal solutions with a small dataset—and we provide a method to identify exactly which data you need,” says Asu Ozdaglar. Learn more and read the paper: https://bit.ly/49sqkNI MIT EECS MIT Civil and Environmental Engineering MIT Institute for Data, Systems, and Society (IDSS) MIT Schwarzman College of Computing MIT School of Engineering

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In the wake of OpenAI announcing Stargate UAE, the new 200MW AI factory in partnership between US <> UAE. We at FLUIX AI are in the ground level in Abu Dhabi this week, meeting with Data Center Operators and Energy allocators. The race for compute is heating up here 🔥 FLUIX enables data centers to double compute capacity be eliminated energy waste inside these data centers using AI control systems. We are excited to build lasting partnerships during our visit to the UAE

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MDPI, Eldar Sultanow, Dr. Mark Tehrani, Siddhant Dutta and Muhammad Shahbaz Khan, “Defining Quantum Agents: Formal Foundations, Architectures, and NISQ-Era Prototypes” This work introduces a formal definition of quantum agents and outlines architectures that integrate quantum computing with agent-based systems. As concrete proof-of-concept implementations, the authors develop and evaluate three quantum agent prototypes: (i) a Grover-based decision agent for quantum search-driven action selection, (ii) a variational quantum reinforcement learning agent for adaptive policy learning in a multi-armed bandit setting, and (iii) an adaptive quantum image encryption agent that autonomously selects encryption strategies based on entropy-driven feedback. These prototypes demonstrate practical realizations of quantum agency in decision-making, learning, and security contexts under NISQ-era constraints. CONCURRENCE  

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Machines Can Think 2026 in Abu Dhabi opened with a striking panel on what it takes to scale AI infrastructure at the level of a nation. We heard about AI factories, sovereign models, partnerships, and the shift from narrow language models to world-scale simulation. But what stayed with me was something deeper—how much of this depends not on the technology itself, but on the institutional "boundary conditions" around it. As a mathematician and educator at NYU Abu Dhabi, this moment felt familiar: a system’s behavior is shaped by its constraints. Compute is essential—but without the right talent, governance, partnerships, and epistemic frameworks, it’s not enough. These are some thoughts I’ve been working through after the Machines Can Think panel — sharing in case they’re useful to others navigating similar questions across education, policy, and tech. https://lnkd.in/eAwfv9qk #ArtificialIntelligence #AIGovernance #AILeadership #TechForGood #FutureOfAI #MachinesCanThink #NYUAD

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