Einstein kept saying for 30 years that physics couldn’t work this way—but it turned out that it does. Imagine two particles are created together and then separated. Now there’s no connection between them—no wire, no signal. You choose one particle. The moment you measure it, you instantly know the state of the other particle—even if it’s in another galaxy with no apparent connection. If you measure particle one, you immediately know the state of particle two. And if you measure particle two, you know the state of particle one. But there’s no visible link between them. So how is this information shared faster than the speed of light? This phenomenon is called quantum entanglement. Einstein believed deep down that there must be some hidden variables or hidden symmetries in nature that connect these particles—it’s just that we don’t know about them yet. He argued this for many years. However, the 2022 Nobel Prize in Physics was awarded for experiments that showed Einstein was wrong. There are no hidden variables—this is simply how nature works.

Engineering is the art of efficiently applying physics Raptor 3 is a marvelous leap forward in rocket engine design. The architecture has been radically simplified....deleting almost all exterior plumbing and embedding the cooling channels directly into the structural walls “The best part is no part.” Every pipe, sensor, or flange deleted is a point of failure eliminated and parasitic mass removed. Because the cooling is internal, Raptor 3 doesn’t even need bulky external heat shields The specs are totally insane: • 280 metric tons of thrust • 330+ bar chamber pressure ➝ Up from 300 bar on Raptor 2 and 250 on Raptor 1. Higher pressure means packing way more combustion energy into a smaller volume, generating massive thrust without making the engine physically larger) • Engine mass dropped to just 1,525 kg ➝ Down from 1,630 kg on Raptor 2 and over 2,000 kg on Raptor 1 • Thrust-to-weight ratio of over 183:1 ➝ This means the engine can lift over 183 times its own physical weight. Every kilogram shaved off the engine structure is an extra kilogram of payload Starship can carry to space This level of efficiency approaches the absolute limits of known physics Raptor 3 drastically increases the payload Starship can deliver to orbit This is the engine that makes life multi-planetary and pushes civilization toward Kardashev Type II Soon it is going to fly....It is pretty sick

College dropout Alakh Pandey cofounded edtech company Physics Wallah in 2020 with his business partner. Physics Wallah offers test prep courses to help students crack entrance exams for engineering and medical colleges. See where he lands on the 2026 #ForbesBillionaires# list: (Photo: Physics Wallah)

What if Your Neural Network Was Forced to Obey Physics? Physics-Informed Neural Networks (PINNs) are neural networks trained to satisfy a differential equation by building the PDE residual directly into the loss. They emerged from a very practical problem...classical PDE pipelines can be brilliant, but they often demand heavy discretization work (meshes, stencils, stability tuning), and the method you build is usually tied to one geometry and one solver setup. A PINN flips the workflow by representing the solution itself as a smooth function uᵩ(x,t) and enforcing the physics everywhere you choose to sample the domain. People often meet PINNs in the least helpful way...via a flashy solution plot, and almost no explanation of what was enforced to get it. In this series we keep the enforcement visible. We pick a differential equation, represent the unknown solution as a flexible function, measure how well that function satisfies the equation across the domain, and train it to reduce that mismatch everywhere we sample. A normal neural net learns from labels...you give it inputs and target outputs. A PINN learns from a differential equation...you give it inputs (x,t) and it gets punished whenever its output fails the PDE. By punish we mean that the loss increases when the mismatch is large we reward it if the loss decreases as the mismatch gets smaller. The network isn’t replacing physics, it’s becoming a flexible function that is forced to satisfy the same calculus you’d impose on any candidate solution. The math breakdown: We start with a PDE we want to solve on a domain Ω. Write it as uₜ(x,t) + N(u(x,t), uₓ(x,t), uₓₓ(x,t), …) = 0 for (x,t) in Ω A PINN replaces the unknown function u with a neural network output uᵩ(x,t) Now define the physics residual by plugging uᵩ into the PDE rᵩ(x,t) = ∂uᵩ/∂t + N(uᵩ, ∂uᵩ/∂x, ∂²uᵩ/∂x², …) If uᵩ were an exact solution, we would have rᵩ(x,t) = 0 everywhere. We may also have data points (xᵢ,tᵢ,uᵢ) from measurements or a known initial condition. The training objective is just a weighted sum of squared errors L(ᵩ) = L_data(ᵩ) + λ L_phys(ᵩ) + L_bc/ic(ᵩ) with L_data(ᵩ) = meanᵢ |uᵩ(xᵢ,tᵢ) − uᵢ|² L_phys(ᵩ) = meanⱼ |rᵩ(xⱼ,tⱼ)|² where (xⱼ,tⱼ) are the collocation points in Ω L_bc/ic(ᵩ) = penalties enforcing boundary conditions and initial conditions The key technical step is that the derivatives inside rᵩ are computed by automatic differentiation ∂uᵩ/∂t, ∂uᵩ/∂x, ∂²uᵩ/∂x², … So we can differentiate the total loss L(ᵩ) with respect to ᵩ and train with gradient descent. This is the whole idea behind PINNs. Learn a function, but make the PDE part of the loss, so the network is trained to be a solution, not just a curve-fitter. In the render, the main 3D surface is the network’s current guess uᵩ(x,t), drawn as a living sheet over the (x,t) plane. Hovering above is the neural scaffold...a visible graph of feature nodes and connections. The bright tension threads are the physics residual rᵩ(x,t): each thread tethers a collocation bead on the sheet up to the scaffold, and it thickens and brightens exactly where |rᵩ| is large (color encodes the sign). As training runs, those threads go slack across the domain not because we hid the error, but because the network has actually been pushed toward rᵩ(x,t) ≈ 0. #PINNs# #PhysicsInformedNeuralNetworks# #ScientificMachineLearning# #PDE# #DifferentialEquations# #Optimization# #MachineLearning# #AppliedMath# #ComputationalPhysics#

The timeline in this image tracks the evolution from linear physics to exponential computation. It highlights how we moved from foundational variables like the Schrödinger equation (1926) to complex operations like Shor’s factoring algorithm (1994). We are currently transitioning from NISQ (Noisy Intermediate-Scale Quantum) devices to algorithmic fault tolerance, scaling the probability of error toward zero.