A neural network that senses stress in parts of any shape express
Designing machines is like cooking: you need to know exactly where the 'dough' will tear. Old computer simulations check every point on a part for hours, like measuring the entire pot to make sure the handle doesn't fall off. The new-generation neural network looks at a part as a mesh of points, like an experienced chef who can instantly sense how heat will spread in any pan. Much like spectroscopy breaks light into colors, it breaks down stresses, revealing weak spots.
Unlike old machine learning methods that got lost with shape changes (accuracy dropping to 1%), this network grasps the essence of the design. It doesn't care about rotations, displacements, or material (carbon fiber, steel) the principle works the same. Accuracy hits 97% in a fraction of a second. Amazingly, the network knows no physics: it's simply trained on examples, like a chef who feels where the dough will tear without a textbook.
When entropy rises, uncertainty due to complex shapes, old methods produce chaotic predictions, while the neural network only slightly reduces accuracy. This approach could become the standard model for rapid strength analysis.
Holes in parts are the most dangerous spots: cracks most often start around them. Engineers call this 'stress concentration'.
In sci-fi movies, AI instantly generates spaceship blueprints. A similar approach is already here: a neural network predicts strength in seconds.