Jay Shah’s Post

🌟 Exploring Computational Fluid Dynamics (CFD) with Quantum Computing 🌟 As quantum computing continues to push boundaries, its intersection with traditional fields like Computational Fluid Dynamics (CFD) is nothing short of revolutionary. Quantum-powered CFD simulations promise unprecedented accuracy and efficiency, opening doors to breakthroughs in industries like aerospace, automotive, and climate modeling. 🚀🌍 Check out this fascinating upcoming presentation on how QAPINN based Quantum CFD is shaping the future of simulations and how the integration of quantum algorithms can drastically enhance problem-solving capabilities: https://lnkd.in/gK_YxkDv 👉 Stay updated on the latest advancements in quantum technology and its applications. 🔗 Follow for more such insights into Quantum Computing and its disruptive potential across various domains.

Meshing in #CFD simulation – is a complex and challenging process! Traditional mesh-based methods in #CFD are computationally expensive and time-consuming too. Our QA-PINN based methods offer a promising mesh-free alternative by leveraging quantum computing. This focused on reducing the number of trainable parameters in the PINN architecture while maintaining accuracy.  This research can advance engineering simulations in many ways such as: Reduced computational cost: QA-PINN can potentially handle complex CFD problems more efficiently than traditional methods. Improved accuracy: QA-PINN can achieve higher accuracy in simulation results due to its enhanced ability to capture complex physical phenomena. Scalability: It is well-suited for large-scale simulations, enabling the modeling of complex systems. This research has the potential to revolutionize #CFD simulations, enabling more efficient and accurate modeling for real-world applications. Abhishek Chopra will be presenting this paper on Quantum-Assisted PINN (QA-PINN) at TODAY at IEEE Quantum Week. #IEEE #Quantum #QML #pinn

Quantum Computational Fluid Dynamics (QCFD) Project 🚀 We're thrilled to announce the launch of our LinkedIn page for the Quantum Computational Fluid Dynamics (QCFD) project, a pioneering initiative funded under the Horizon 2020 framework to revolutionize fluid dynamics through quantum computing. 🔍 The QCFD project aims to leverage the unprecedented capabilities of quantum computing to solve complex fluid dynamics problems faster and more accurately than ever before. Our multinational team of experts is working at the cutting edge of technology and science to create simulations that could transform industries ranging from aerospace to automotive and beyond. 🌐 Learn more about our mission, research, and the people behind the innovation by visiting our website: www.qcfd-h2020.eu 👥 Follow us to stay updated on our progress, insights, and breakthroughs as we advance the frontiers of both quantum computing and computational fluid dynamics. 💡 We believe in the power of collaboration and are open to engaging with industry and academic partners interested in quantum technology and fluid dynamics. Reach out to explore potential collaborations or to simply connect with our team. Join us on this exciting journey to redefine the possibilities of computational fluid dynamics with quantum technology! University of Hamburg ,Dieter Jaksch Hamburg University of Technology, Thomas Rung Forschungszentrum Jülich, Felix Motzoi Technical University of Munich, Barbara Kraus ENGYS, Paolo Geremia Technical University of Crete, Dimitrios Angelakis planqc, Martin Kiffner #QCFD #QuantumComputing #FluidDynamics #Innovation #H2020 #QuantumTechnology #Quantum #QuantumComputationalFluidDynamics 

Challenges in classical #CFD and the promise of #quantum computing In our newest blog post from the Quanscient Quantum Labs by Prof. Ljubomir Budinski, we explore how quantum computing can address the limitations of traditional CFD in airfoil design. While CFD is essential for understanding fluid behavior, it struggles with complex problems such as accurately simulating airflow, especially turbulence, which demands significant computing power. This challenge is amplified when dealing with multiphysics simulations that involve multiple physical processes, such as heat transfer alongside airflow. Professor Budinski discusses a new quantum algorithm specifically designed for airfoil simulations, demonstrating its potential to efficiently handle these complex multiphysics problems. This research could lead to more accurate and innovative airfoil designs, with potential benefits across various fields. Read the full article below to learn more about our latest developments in quantum computing and CFD. https://lnkd.in/dD9t_tXF #quantumcomputing #CFD #airfoil #multiphysics #simulation #quanscient

During conferences, simulation engineers often ask me on how exactly is the quantum approach better. Here's a simple explanation with a short example: To find the shortest exit path in a maze which option would you choose? 1. Explore each path one by one and iteratively arrive at the shortest path 2. Get an aerial view of the maze and arrive at the shortest path instantly The maze analogy (and specifically option 2) explains how quantum algorithms approach complex simulations, as compared to classical methods BosonQ Psi (BQP)'s latest blog on this subject provides more details. Link to blog in the comments section #quantumcomputing #quantum #algorithms #quantumtechnology #simulation #simulationtechnology #Engineering #design

While #machinelearning #interatomicpotential (MLIP) plays an increasingly important role in #computational chemistry and #materials science, they often lack generalization capability and robustness during atomistic simulations, yielding unphysical energy and force predictions that hinder their real-world applications. We address this challenge by introducing a #physicsinformed, #weaklysupervised approach. We introduce two novel loss functions, extrapolating the potential energy via a Taylor expansion and using the concept of conservative forces. Our approach improves the accuracy of MLIPs applied to training tasks with sparse training data sets and reduces the need for pre-training computationally demanding models with large data sets. We also show that our approach facilitates MLIPs' training in a setting where the computation of forces is infeasible at the reference level, such as those employing complete-basis-set extrapolation. Our work has appeared on arXiv: https://lnkd.in/eNEdDSTP.

Breaking boundaries in computational fluid dynamics with a quantum twist 🚀 Altair and the Technical University of Munich [TUM] are shaking up the world of computational fluid dynamics [CFD] by introducing the power of quantum computing. 🟠 This cutting-edge collaboration fuses Altair's tech mastery with TUM’s academic excellence. 🟠 The mission? To craft a quantum algorithm using the Lattice Boltzmann Method – a technique that simulates fluid flow like no other. 🟠 With quantum computing's power, we might soon witness simulations in aerospace or automotive sectors that are not only faster but also more precise. 🟠 Let's be honest: Classical computing has its limits. Quantum power is here to tackle complex and time-consuming tasks, maximizing efficiency and accuracy. As traditional computing methods hit the ceiling of their capabilities, could quantum computing be the superhero CFD has been waiting for?💥 #QuantumComputing #CFD #Innovation #Technology #Altair #TUM #LatticeBoltzmannMethod #FluidDynamics #ComputationalScience #TechRevolution

Interested in #Building_integrated #Wind_energy, #Machine_learning, #Artficial_neural_networks (#ANN), #Computational_fluid_dynamics (#CFD) or #Optimization? Check out the latest original research article resulted from my #PhD thesis in Eindhoven University of Technology, recently published in Elsevier's "Energy Reports", titled: "Wind energy harvesting with building-integrated ducted openings: CFD simulation and neural network optimization" #Openaccess link: https://lnkd.in/epNfGFcH #RenewableEnergy, #UrbanWind, #Aerodynamics, #DataDrivenDesign, #MachineLearningInEnergy, #OptimizationAnalytics, #DesignOfExperiments, #DataAnalytics

In case you missed it, earlier this month we published an exciting scientific article with our amazing collaborators at Technical University of Munich and University of Graz! 👨🔬👩🔬👏 By adding stochastic correction terms to the Maxwell-Bloch equations, we developed an enhanced semiclassical model which compensates for the lack of quantisation in the electromagnetic field in Maxwell's equations. We thus succeeded in fully accounting for the quantum behaviour of light, yet retained the advantages of a semiclassical model permitting to describe interference and propagation effects in realistic, spatially extended devices. Way to go!! 🚀🚀 #QuantumTech #Innovation #QuantumComputing #Quantum #Semiconductors #DigitalTwins #Simulation Michael Haider https://lnkd.in/ghvUkFnd