Ansys AI’s Post

I've seen the alarm being raised about the private sector "vacuuming up" academic/scientific AI talent. Fear not, this is a normal part of a paradigm shift. "The work" that needs doing most is exploiting the current science breakthrough, i.e. "engineering". Its iterative. The engineering/business work will find the real limits of the current science and the energy will reenter science and academia, armed with real world findings. Rinse, repeat.

Join the Artificial Intelligence and Materials Science virtual workshop hosted by MRS Communications. Discussions will include new AI methods and approaches, computational models, science and engineering domains, materials development, and applications with new ecosystems including advanced manufacturing. Register to discuss these topics with experts here: https://lnkd.in/eFReuVhA #ArtificialIntelligence #AI #MachineLearning #Robotics

FAQ #7: What are the short-term and long-term business goals? In the short term, the goal is to create a sustainable global environment through the global dissemination and adoption of AI smart electronic generator technology. In the long term, the foundation aims to operate as a global aerospace research institute through future innovative technologies, contributing to the development of Earth science on a global scale.

🚀 Exciting news! Our latest research on Distributed Federated and Incremental Learning for Electric Vehicles Model Development in Kafka-ML has just been published at the 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM). This paper presents a breakthrough in managing Electric Vehicles (EV) data by integrating Federated Learning (FL) for privacy-preserving data sharing, Incremental Learning for continuous model training, and Distributed Neural Networks (DNN) to minimize latency and improve response times. Our work within the Kafka-ML framework allows real-time EV data to enhance user experiences and better understand charging behavior, all while maintaining privacy and security in a Vehicle-to-everything (V2X) context. 👉 You can read the full paper here: https://lnkd.in/dgadgMFP 🙏 A big thank you to our co-authors Omer Waqar (Ph.D., P.Eng, Senior Member IEEE), Cristian Martin Fernandez, and Manuel Diaz for their contributions. Also to the collaborators and funding agencies for their support throughout this project. We hope this research sparks further innovation in AI, EV technologies, and streaming data processing. 💡 #research #WINCOM2024 #KafkaML #federatedlearning #incrementallearning #electricvehicles #AI #machinelearning #V2X #streamingdata #EVtech #EVOLVE

https://lnkd.in/grqnb4Ue A follow up to yesterday's post, Dotmatics CEO Thomas Swalla talks about #digitaltransformation in the life and materials sciences

How do you leverage AI to develop a pump failure prediction model? That’s the problem students from Texas A&M University College of Engineering tackled as part of their #SamsungAustinSemiconductor sponsored capstone project. Watch to learn more about how they developed the model and how it can be applied at the #SamsungAustinSemiconductor site. https://lnkd.in/dMFfRmsw Ethan Treadway Cesar Garza #WorkforceDevelopment #TalentPipeline

Fueled by Innovation, Driven By Research: Edmund Yeh - Part 3 of 5 Gain insight into the real-world impact of Edmund's work in networked machine learning. Tune in all week to learn more about Dr. Edmund Yeh Northeastern University College of Engineering #techtransfer #innovation #electricalengineering #computerengineering

🧬 How the Materials Genome Initiative Brought Today’s Innovations 🧬 The Materials Genome Initiative (MGI) was launched by the U.S. government in 2011 to revolutionize how we discover and deploy new materials by leveraging data-driven design, computational modeling, and high-throughput testing. The goal? To cut development time in half and bring innovative materials to market faster. At Materia Chemistries, we’re building on this framework using generative AI to accelerate the discovery of high-performance materials specifically for coatings and surface treatments. Our focus is on addressing major pain points like corrosion, fouling, and thermal degradation to extend the lifespan of critical energy infrastructure. By integrating AI with materials science, we aim to develop solutions that will enable climate technologies to operate reliably in the most demanding environments, supporting a more resilient and sustainable future. #MaterialsScience #Innovation #ClimateTech #AI #MaterialsGenomeInitiative Photo by Sangharsh Lohakare on Unsplash

🖥️ Scientists have developed an AI-based method that helps gather data more efficiently in the search for new materials, allowing researchers to navigate complex design challenges with greater precision and speed. Read more: https://lnkd.in/gc8M7fA6

Faster the better