Artificial intelligence and machine learning are increasingly used to accelerate multiphysics simulations, reduce computation times, automate workflows, and support inverse problem-solving in engineering design.

PINNs aim to replace classical solvers like FEM by directly embedding physics equations and boundary conditions into neural networks, offering massive parallelization but currently limited by accuracy and high GPU costs.

These models learn from the outputs of classical solvers and act as ultra-fast approximations, enabling rapid exploration of design spaces. They depend heavily on large training datasets but can outperform FEM in speed once trained.

Combining AI-based surrogates with classical solvers offers both speed and accuracy. Engineers can filter many design options quickly with surrogates, then verify the most promising ones using traditional methods.

Large language models and multimodal AI are increasingly applied to assist engineers with setup, material property definition, workflow automation, and anomaly detection, preventing wasted simulation runs and saving time.

Major challenges include accuracy gaps in AI solvers, dependence on scarce training data, and the need for expensive GPU resources. Neural network architectures and optimization methods also require further innovation to handle complex physics.

Unlike others who rely on limited public datasets, Quanscient can massively generate diverse training data on the cloud, strengthening neural surrogate performance and ensuring better generalization.

Hybrid models blending AI and classical solvers are expected to dominate, with increasing emphasis on verification and interpretability. AI agents will become more integrated into engineering workflows.