Загрузка видео...

Не удалось загрузить видео

На главную

If you work in Physics, Machine Learning, Engineering, or any field with a serious mathematical component, Space Mapping is one of those ideas worth adding to your toolkit.

27,099 просмотров • 2 месяцев назад •via X (Twitter)

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

The Machine That Learns The Law Behind The Data A very very interesting US Patent US10963540B2 - Physics Informed Learning Machine describes a learning system that does not begin with data alone. It begins with a physical model, usually written as a differential equation (or PDE) dx/dt = f(x,t) A normal Machine Learning model sees scattered data and tries to fit it. A physics-informed learning machine starts with a law. Then it treats the data as evidence that updates what the model believes about the physical system. For this application, I use the patent idea on NASA C-MAPSS Turbofan engine data. The machine watches multivariate telemetry from a degrading engine and infers a hidden health state that is not measured directly. From that posterior belief, it estimates the engine’s remaining useful life. In the main 3D scene, the engine lifetime is turned into a tunnel. The spiral ribbons are real sensor channels evolving over cycle-time. The glowing core is the inferred health state. The surrounding cloud is uncertainty. The orange wall ahead is the predicted failure horizon. So the big picture is: sensor evidence comes in, posterior belief tightens, and the machine moves from uncertainty toward a concrete failure prediction. The inset posteriors make that explicit. The health posterior shows where the model believes the hidden engine condition sits at the current moment, and how sharply it believes it. The RUL posterior shows the same idea for remaining life... early on it is broad, later it shifts left and narrows as the machine becomes more certain about how close failure is. This idea is not limited to engines. The same idea can apply to data centers, CPUs, GPUs, cooling systems, power grids, robotics, batteries, and any machine that produces telemetry while obeying physical constraints. In an age where machine learning runs on massive hardware infrastructure, this kind of model matters: it can turn noisy sensor streams into early warnings before expensive systems fail.

Mathelirium

17,696 просмотров • 1 месяц назад

This isn't VFX visual effects...it's actual freakin physics! 🤯🤩🤗🙌🏾 You're watching the physics of space and matter coevolve...a small window into the kind of world Wolfram Physics Wolfram predicts. Every ripple, collision, and shimmer you see is a causal event in a living hypergraph. Space itself is an active network of nodes and links...springs and diagonals...constantly stretching, relaxing, and rewriting as the system evolves. Each interaction you see...a nutrient diffusing through the medium, a burrower tunneling, a tentacle feeling drag...is a computation of spacetime itself. Every connection that forms or breaks is an update to the causal structure: a new link, a new moment in the unfolding hypergraph universe. Our environment behaves like an adaptive fabric...the lattice tightens where organisms stir it, loosens where they pass. Chemical fields spread through those same links, feeding back into motion and growth. When the cluster pulsates, it's literally reorganizing local causal geometry...matter and space changing together!👌🏾 We even test causal invariance frame by frame, swapping operation orders to ensure that spacetime remains consistent no matter the update path. That's not animation logic...that's a computational physics experiment running live!🥳 Tentacles feel friction, burrowers dig through gradients, and behind them the lattice stiffens, storing history as structure. #Mathematics #Physics #WolframPhysics #Mathelirium

Mathelirium

20,090 просмотров • 7 месяцев назад