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When gravitational potential rotates, it can lock onto an orbit’s phase and start rearranging motion. Order can turn into visible structure, and structure can slide into chaos, without any collisions or extra bodies. This is a toy barred galaxy...a softened central potential plus a rotating bar-shaped overdensity. The bar's...

10,118 просмотров • 6 месяцев назад •via X (Twitter)

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Today we introduce Stochastic Differential Equations (SDEs), and the main thing to watch for is this: We’ll use Brownian motion as the basic noise source, then see how well-known SDEs drop out of it naturally, without guessing. I still think the best way into these concepts is through an application. We look at the theory behind electromagnetic scattering and radar clutter, which leads straight into anomaly detection on scattering statistics. When a narrowband wave scatters off a messy cloud of particles, the complex field at your receiver is a random phasor sum. At time t you can write the electric field as E_N(t) = Σⱼ₌₁ᴺ e^{iθⱼ(t)}, each term a unit arrow in the complex plane from scatterer j. This is exactly where Brownian motion shows up in the most reasonable way. Think of all the microscopic chaos: tiny motions, index fluctuations, path jitters, Doppler shifts. Over short times, all of that shows up as small random kicks to the phases θⱼ(t). If you made θⱼ(t) random in an ad-hoc way, like resampling a fresh independent angle at every instant, the field would jump around unrealistically with no physical time structure. Brownian motion is what you get when each phase takes the continuous-time limit of many tiny, independent kicks. It’s continuous in t, its variance grows the right way, and it carries just enough temporal structure to look physical. So we model each phase as a Brownian walk, θⱼ(t) = θⱼ⁰ + σ_θ Bⱼ(t), with independent Brownian motions Bⱼ(t) and a phase-diffusion rate σ_θ. Brownian motion here isn’t window dressing. It’s the clean way to compress all the small random stuff into a single process that actually matches how phases wander in time. This is called Rayleigh Scattering, but the same sum of many tiny coherent echoes shows up in lots of places...e.g. wireless multipath fading (phones/Wi-Fi), laser/optical links through atmospheric turbulence, ultrasound speckle in tissue, and sonar/underwater acoustics in rough or bubbly water. #StochasticProcesses #BrownianMotion #ItoCalculus #RadarClutter #RayleighScattering #SignalProcessing

Mathelirium

31,182 просмотров • 6 месяцев назад

🧱 #Live3D #Live2D #HandTracking 🧱 I spent some time refining the way I use Warp Deformers to create lateral physics-based swinging motion in Live2D. With a new production concept, I tried to achieve the highest possible level of mathematical precision. At the core are two fundamental swinging structures, which I can mix in different proportions to create different qualities of motion. The first is parallel swinging. Both sides of the Warp Deformer remain perfectly parallel throughout the motion, making it look somewhat like a sheet of paper or a tassel. The second is bending swinging. The angle between the two sides of the Warp Deformer changes as it moves, making it look more like a flexible tube or a tail. (0:12)Interestingly, when I layer the first and second structures together, the bottom forms an X-shaped pattern as it swings. It looks a little like a spine or some kind of mechanical structure. (0:23)The third structure is an equal blend of the first and second. Its characteristics are much more neutral, combining the qualities of both. (0:35)The fourth and fifth structures are created by smoothly blending the first structure into the center of the second, and the second into the center of the first, respectively. This makes them appear softer and more flexible, somewhat like a piece of fabric. (0:28)Interestingly, when I blend the fourth and fifth structures equally, the result is exactly identical to the third structure. This also demonstrates that, despite their apparent complexity, the two are actually perfect mirror images of each other. Every physics-based motion shown here uses a two-segment pendulum structure, with a swinging range of ±90°. In theory, when curled to its limit, it can bend upward into a perfect semicircle. To most people, this may look as boring as a brick. But to me, this is exactly where the romance of Live2D physics lies. Does anyone else feel the same way?

📐Hephaestus📏Live2D匠人魂

19,362 просмотров • 21 дней назад

Today we introduce Stochastic Differential Equations (SDEs). I find that the best way to introduce these complex concepts is to look at an application. This is part I of the lecture🙂 We look at the theory behind electromagnetic scattering/radar clutter which leads to anomaly detection on scattering statistics. When a narrowband wave scatters off a messy cloud of particles, the complex field at your receiver is a random phasor sum...at time t you can write the electric field as E_N(t) = Σⱼ₌₁ᴺ e^{iθⱼ(t)}, each term a unit arrow in the complex plane from scatterer j. This is exactly where the magic of Brownian motion appears naturally and in the most reasonable way. Think of all the microscopic chaos...tiny motions, index fluctuations, path jitters, Doppler shifts that shows up as small random kicks to the phases θⱼ(t) over very short times. If you just made θⱼ(t) random in an ad-hoc way (say, resampling independent angles at each time), the field would jump around unrealistically with no temporal structure. Brownian motion is what you get when you let each phase take the continuous-time limit of many tiny, independent kicks...it’s continuous in t, it has the right cumulative variance growth, and it remembers just enough of its past to look physical. So we model each phase as a Brownian walk, θⱼ(t) = θⱼ⁰ + σ_θ Bⱼ(t), with independent Brownian motions Bⱼ(t) and a phase-diffusion rate σ_θ. Brownian motion here isn’t window dressing...it’s the clean way to compress all the small random stuff into a single process that actually matches how the phases wander in time. #StochasticProcesses #BrownianMotion #ItoCalculus #RadarClutter #RayleighScattering #SignalProcessing

Mathelirium

55,319 просмотров • 8 месяцев назад