5don MSN
Automating microfluidic chip design: Hybrid approach combines machine learning with fluid mechanics
Researchers led by Assoc. Prof. Dr. Savaş Taşoğlu from the Department of Mechanical Engineering at Koç University have ...
2UrbanGirls on MSNOpinion
Neel Somani on formal methods and the future of machine learning safety
Neel Somani has built a career that sits at the intersection of theory and practice. His work spans formal methods, mac ...
Wilmington, Delaware - February 03, 2026 - PRESSADVANTAGE - PSCI shared perspective on staffing and consulting ...
An agentic AI tool for battery researchers harnesses data from previous battery designs to predict the cycle life of new battery concepts. With information from just 50 cycles, the tool—developed at ...
When experiments are impractical, density functional theory (DFT) calculations can give researchers accurate approximations of chemical properties. The mathematical equations that underpin the ...
A recent study, “Picking Winners in Factorland: A Machine Learning Approach to Predicting Factor Returns,” set out to answer a critical question: Can machine learning techniques improve the prediction ...
A new technical paper titled “Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogate” was published by researchers at KTH Royal Institute of Technology and ...
Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...
An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy inefficiency in power amplifiers for next-generation wireless communication.
Some results have been hidden because they may be inaccessible to you
Show inaccessible results