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Engineering Photonics Semiconductor

Micro LEDs for AR Smart Glasses: How the Photonics Behind Tiny Displays Actually Works

Augmented reality smart glasses impose an unusually severe set of requirements on a display. The image source must be physically small enough to fit inside a temple or frame, bright enough to remain visible against the…

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Research Cloud Engineering

Programmable Cloud Laboratories: Will Researchers Soon Conduct Experiments Remotely?

Scientific computing underwent a structural change when researchers stopped treating computation as a machine they had to own and began treating it as a service they could request. Programmable cloud laboratories propose…

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Research Engineering Manuscript

How to prepare choose keywords for research articles in Q1 journals

Keywords are a small component of a research manuscript, but they perform several important functions. They help journal submission systems classify a paper, support editor and reviewer assignment, contribute to…

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simulation Physics Solver

How Should Researchers Validate AI Models Trained on Simulation Data?

Artificial intelligence models trained on simulation data are increasingly used as surrogate models for finite element analysis, computational fluid dynamics, molecular simulation, photonic design, structural mechanics,…

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simulation Physics Solver

Neural Operators and Physics Informed AI : Can Machine Learning Replace Traditional Solvers?

Machine learning is increasingly being presented as a new computational engine for physics and engineering. Physics informed neural networks, Fourier neural operators, DeepONets, graph based operator models, and related…

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Blog FEA General Knowledge Research Science

FEA optimization using Deep Learning

FEA optimization using Deep Learning: Finite Element Analysis (FEA) is a widely used computational method for solving structural, thermal, and fluid dynamics problems in engineering. However, traditional FEA methods are computationally intensive, particularly for optimization tasks that require multiple simulations. Recent advancements in Deep Learning (DL) have shown immense potential in accelerating FEA, reducing computational […]

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Blog Engineering FEA Research

The Finite Element Method

Introduction The Finite Element Method (FEM) is a powerful numerical technique used to solve complex engineering and physical problems. It has become an indispensable tool in various fields, including structural analysis, heat transfer, fluid dynamics, electromagnetism, and biomechanics. The method’s versatility and robustness make it applicable to a wide range of real-world problems, from designing […]

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Blog

The Toolbox in ANSYS Workbench

The Toolbox in ANSYS Workbench is the gateway to building your simulation workflow. It provides a structured and categorized list of systems and tools that users can drag and drop into the Project Schematic to set up their analyses. Each category in the toolbox is designed to address a specific aspect of simulation or to […]

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Research AI Revolution Blog Engineering FEA simulation

Aerospace Engineering Made Easy with NASTRAN

Aerospace engineering involves some of the most complex and critical design challenges, requiring precision, reliability, and performance. NASTRAN, developed by NASA and now maintained by Siemens, has become a cornerstone in the aerospace industry for structural analysis. This in-depth guide explores how NASTRAN simplifies aerospace engineering, its key features, and its applications in real-world scenarios. […]

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Blog

Potential of Multibody Dynamics with MSC Adams

Multibody dynamics (MBD) is a crucial aspect of engineering simulations, particularly in the automotive, aerospace, and robotics industries. MSC Adams, the world’s leading MBD software, offers unparalleled capabilities for modeling, simulating, and analyzing the dynamic behavior of interconnected rigid or flexible bodies. This blog post will delve into the features and applications of MSC Adams, […]