In the domain of metamaterials, the push toward automated design has been accelerated by advances in generative machine learning. The advent of deep ...

Neural architecture search (NAS) and machine learning optimisation represent rapidly advancing fields that are reshaping the way modern systems are designed and deployed. By automating the process of ...

Abhijeet Sudhakar develops efficient Mamba model training for machine learning, improving sequence modelling and ...

Until now, designing complex metamaterials with specific mechanical properties required large and costly experimental and simulation datasets. The method enables ...

AI tools like ChatGPT and Gemini are transforming the way we work. But architecture firms are using artificial intelligence and machine learning for more than just automation and email writing. Many ...

The convergence of artificial intelligence, cloud-native architecture, and data engineering has redefined how enterprises ...

(A) Inverse control architecture, which can reproduce the strain field by inputting target strain field images. (B) Forward control architecture, which can predict the strain field by inputting the ...

As organizations plan for 2026, a clear structural shift is emerging in how technical talent is valued and deployed. Amid this shift, Interview Kickstart has introduced an advanced machine learning ...

More aggressive feature scaling and increasingly complex transistor structures are driving a steady increase in process complexity, increasing the risk that a specified pattern may not be ...

This diagram illustrates how the team reduces quantum circuit complexity in machine learning using three encoding methods—variational, genetic, and matrix product state algorithms. All methods ...

Researchers have found a way to make the chip design and manufacturing process much easier — by tapping into a hybrid blend of artificial intelligence and quantum computing. When you purchase through ...