Loading video...

Video Failed to Load

Go Home

37,361 views • 5 days ago •via X (Twitter)

0 Comments

No comments available

Comments from the original post will appear here

Related Videos

When I first took ordinary differential equations, the pattern was always the same. Week 1 turns into a speedrun of methods: separation of variables, integrating factors, variation of parameters, Bernoulli, exact equations… and by Week 2 or 3 the course has quietly degenerated into hammer-picking. Spot the form, apply the recipe, move on. Mechanical! Fuuuuck!😫😫😫😫 The problem is what you don’t walk away with. You leave with a toolkit, but without a feel for what a differential equation even is, especially geometrically. And that’s a big deal, because in real modeling the equations you meet are rarely nice enough to reward memorized recipes. So you end up trained to solve toy forms, while the actual subject...the behavior, the flow, the shape of solutions stays blurry. This is why I’m biased toward the old-timers. Their old-school way of doing things always surprises me:...they’ll spend time on one idea until it sticks, instead of sprinting through a syllabus checklist. One lecture from them and you start noticing a contrast. A lot of modern teaching feels like "finish the content,". You get marched through techniques, but you’re not left with a single thought that keeps bothering you later...the kind of thought that actually pushes you toward research-level curiosity. MIT OpenCourseWare’s Professor Arthur Mattuck did that to me in his very first ODE lecture. One lecture, and your whole relationship with dy/dx = f(x,y) changes. In this segment, Prof. Mattuck is basically saying: A first-order ODE is a slope field, and a solution is a curve that moves everywhere tangent to that field. The math breakdown Write the ODE as dy/dx = f(x,y). At each point (x,y) you attach a tiny line segment with slope f(x,y). A function y = y₁(x) is a solution exactly when its graph follows those slopes:. At every x, the slope of the curve equals the slope prescribed by the field at the point on the curve. That’s the single line that unifies both viewpoints: y₁′(x) = f(x, y₁(x)). So solving the ODE and drawing an integral curve are the same statement in two languages!👌🏻 Once you see that, you can stop obsessing over whether you can write y(x) in closed form. You can start asking the questions that matter: where do solutions flow, where do they get trapped, where do they blow up, and where does existence/uniqueness fail just because the field isn’t even defined? That’s the perspective shift I wish every ODE course forces early and it’s exactly why I keep pairing math with animation. #DifferentialEquations #ODEs #VectorFields #MathAnimation #Mathematics

Mathelirium

53,338 views • 6 months ago

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 views • 6 months ago

Ask anyone who’s taken a course in Ordinary Differential Equations (ODEs) what a solution to an ODE represents geometrically, and most of them won’t have a clean answer. When I first took ordinary differential equations, the pattern was always the same. Early on it turns into a speedrun of methods: separation of variables, integrating factors, variation of parameters, Bernoulli, exact equations. Then pretty quickly the course slides into hammer-picking. Spot the form, apply the recipe, move on. Too mechanical! And the real problem is what you don’t walk away with. You leave with a toolkit, but without a feel for what a differential equation even is, especially geometrically. That matters because in real modeling the equations you meet are rarely nice enough to reward memorised recipes. So you get trained to solve toy forms, while the actual subject stays blurry. The behavior. The flow. The shape of solutions. It wasn't until I watched the first lecture of Professor Arthur Mattuck that I realized I didn’t actually know what a solution to a differential equation represents geometrically. His point is almost embarrassingly simple. A first-order ODE is a slope field, and a solution is a curve that stays tangent to that field everywhere. The math breakdown: Write the ODE as dy/dx = f(x,y). At each point (x,y), attach a tiny line segment with slope f(x,y). A function y = y₁(x) is a solution exactly when its graph follows those slopes. At every x, the slope of the curve equals the slope prescribed by the field at the point on the curve. That’s the one line that ties both viewpoints together: y₁′(x) = f(x, y₁(x)). So solving the ODE and drawing an integral curve are the same statement in two languages. Once you see that, you stop obsessing over whether you can write y(x) in closed form. You start asking the questions that actually matter. Where do solutions flow. Where do they get trapped. Where do they blow up. Where does existence or uniqueness fail because the field isn’t even defined? That’s the perspective shift I wish every ODE course forces early. It’s also why I keep pairing math with animation. #DifferentialEquations #ODEs #VectorFields #AppliedMathematics #Mathematics #

Mathelirium

40,739 views • 5 months ago

Lecture 3 of our Quantum Mechanics series. Lecture 2 gave us the one clean privilege quantum theory offers: treat ψ(x,t) as the state and ρ(x,t) = |ψ(x,t)|² as probability, because Schrödinger evolution forces ρ to obey a continuity equation. Lecture 3 is what that continuity equation is really telling you. If ρ behaves like a fluid, then the only question that matters is: What is the velocity field? Write ψ(x,t) = r(x,t) exp(i θ(x,t)). The magnitude r sets how much probability is sitting there. The phase θ sets where it tries to go. When you unpack the current j = Im(ψ* ∇ψ), it collapses to j = (ρ/m) ∇θ, which means the flow lines you draw are literally contours of phase geometry. Then the constraint that makes the picture bite: ψ has to be single-valued, so θ can’t wind by an arbitrary amount. Around any closed loop the total phase change must be 2π n, with n an integer. That’s why vortices aren’t features you add...they’re defects the math permits, in quantized units. In the render you see both layers at once...the 3D surface shows |ψ| breathing while the phase skin slides, and the 2D panel exposes the engine...current lines steering around discrete vortex charges. The math breakdown We write the state as a complex field ψ(x,t) on the plane (x in R²). The Born rule defines the probability density ρ(x,t) = |ψ(x,t)|² Schrödinger evolution (ħ = 1 units) is i ∂ψ/∂t = [ −(1/2m) ∇² + V(x,t) ] ψ Now derive conservation of probability. Start with ρ = ψ*ψ: ∂ρ/∂t = ψ* (∂ψ/∂t) + ψ (∂ψ*/∂t) Use Schrödinger and its complex conjugate: ∂ψ/∂t = (1/i) [ −(1/2m) ∇²ψ + Vψ ] ∂ψ*/∂t = (−1/i) [ −(1/2m) ∇²ψ* + Vψ* ] Substitute. The V terms cancel, and the remaining terms rearrange into the continuity equation ∂ρ/∂t + ∇·j = 0 with probability current j = (1/2mi) ( ψ* ∇ψ − ψ ∇ψ* ) = (1/m) Im(ψ* ∇ψ) So "probability density" really behaves like a conserved fluid density with flux j. Now expose the phase mechanism. Write ψ in polar form ψ(x,t) = r(x,t) exp(i θ(x,t)) Compute the gradient ∇ψ = exp(iθ) (∇r + i r ∇θ) Then ψ* ∇ψ = r (∇r + i r ∇θ) Taking the imaginary part gives Im(ψ* ∇ψ) = r² ∇θ = ρ ∇θ So the current becomes j = (ρ/m) ∇θ That’s the steering-wheel statement: Phase gradient sets the flow direction and speed (modulated by density and m). Finally, quantized vortices. Because ψ must be single-valued, going around any closed loop must return the same complex value. That forces the phase winding to be an integer multiple of 2π: ∮ ∇θ · dl = 2π n with n in Z n is the vortex charge. Vortex cores sit where ρ ≈ 0 (phase is undefined), and the current streamlines circulate around them. #QuantumMechanics #Wavefunction #SchrodingerEquation #BornRule #ProbabilityCurrent #ContinuityEquation #Phase #Vortices #TopologicalDefects #ComplexAnalysis #MathematicalPhysics #Mathematics #Physics

Mathelirium

37,998 views • 6 months ago

The Magnetobiology Episode: A company in San Francisco, called Nonfiction Laboratories, is building proteins (like antibodies and enzymes) that can be controlled using small magnets. In this episode, co-founder Maria Ingaramo and scientific advisor Andrew York explain how they engineered a protein, MagLOV, that responds strongly to magnetic fields, why most prior attempts have failed to replicate, and how the mechanism of magnetically-controlled proteins actually works. They also get into the “dream” use cases, like cancer drugs that activate only at the tumor, which might have a lower toxicity inside the body. This podcast is made possible by Astera Institute. I'm happy with how this episode came out. I think my interviewing skills are improving, and I'm getting better at building up context throughout the episode. Enjoy! Search for "The New Biology" on YouTube, Spotify, and Apple Podcasts. Timestamps: 00:00 - Opening 00:54 — Introduction 01:35 — The dream 05:38 — Why magnets vs. light or ultrasound 10:05 — The physics 17:48 — On the name "magnetogenetics" 21:25 — Birds and cryptochromes 27:09 — Why is the field filled with so much junk? 29:51 — Adam Cohen's molecule 33:24 — Markus Meister’s debunking 38:06 — The experiment 46:22 — Finding the LOV domain 54:11 — Singlets, triplets, and cysteine 56:54 — What the magnet is actually doing 1:05:13 — The conformational-change red herring 1:12:46 — The Quantum Biology Institute 1:19:31 — Founding Nonfiction Labs 1:24:38 — How to convince skeptical investors 1:29:39 — What a magnetogenetic medicine might look like 1:38:50 — First clinical indications 1:45:12 — The regulatory path 1:48:01 — What the field needs 1:54:30 — Appendix: Whiteboard lecture

Niko McCarty.

44,908 views • 1 month ago

Quantum Mechanics Series Lecture 4 Lecture 1 established that ρ(x,t) = |ψ(x,t)|² behaves like a conserved probability density. Lecture 2 showed what drives that flow. We also saw that writing ψ = r exp(iθ) makes the probability current proportional to the phase gradient, making it clear that phase geometry literally steers the motion. Lecture 3 then showed that the centroid of that flow can move almost classically when the packet is tight and the external potential is smooth. However, that raises yet another question. If the centroid can look classical, why does the full wave still spread, bend, split, and interfere in ways no classical particle cloud would? This is because the wave is not driven only by the external potential. It is also driven by its own curvature. Write ψ(x,t) = r(x,t) exp(iθ(x,t)) with ρ = r². Then Schrödinger’s equation gives two coupled real equations. One is the continuity equation you already know. The other looks like a Hamilton-Jacobi equation, but with one extra term: Q = −(1/2m) ∇²r / r This is the so-called Quantum Potential. It depends entirely on how the amplitude bends across space. So, the wave is being shaped not only by V(x,t), but also by the geometry of its own envelope. In the animation, the upper surface is still |ψ| and its skin is still colored by arg(ψ). The glowing threads still trace the probability current. But now a second membrane hangs underneath. That lower membrane encodes the quantum potential Q itself. The porcelain bead marks the quantum centroid. The amber bead follows a classical centroid under the same external V. When those paths separate, the lower membrane tells you why. The difference is not magic but the extra term classical mechanics does not have. The math breakdown: Start from Schrödinger evolution in units with ħ = 1: i ∂ψ/∂t = [ −(1/2m) ∇² + V(x,t) ] ψ Write the state in polar form: ψ = r exp(iθ) Then ρ = |ψ|² = r² From the imaginary part, you recover probability conservation: ∂ρ/∂t + ∇·j = 0 with j = (1/m) Im(ψ* ∇ψ) = (ρ/m) ∇θ So the local velocity field is v = j / ρ = ∇θ / m Now take the real part of Schrödinger’s equation. That gives ∂θ/∂t + |∇θ|² / (2m) + V + Q = 0 where Q = −(1/2m) ∇²r / r This is the classical Hamilton-Jacobi equation with one extra term. That extra term is what makes quantum motion locally different from classical motion. Take a gradient of that phase equation and use v = ∇θ / m. Then the flow obeys an Euler-like equation: ∂v/∂t + (v·∇)v = −(1/m) ∇(V + Q) In other words, there are really two forces in the problem. One comes from the external potential V. The other comes from the wave’s own curvature through Q. That is why Ehrenfest is only approximate. The centroid can still satisfy d⟨x⟩/dt = ⟨p⟩/m d⟨p⟩/dt = −⟨∇V⟩ but the internal shape of the packet evolves under the combined influence of V and Q. When the packet stays broad and smooth, Q is gentle and the motion looks more classical. When the packet develops sharp curvature or interference structure, Q becomes strong and the classical picture breaks down. That is what this scene is designed to show live. #QuantumMechanics #Wavefunction #SchrodingerEquation #BornRule #ProbabilityCurrent #ContinuityEquation #Phase #EhrenfestTheorem #QuantumPotential #Madelung #HamiltonJacobi #MathematicalPhysics #Mathematics #Physics

Mathelirium

20,456 views • 3 months ago

Marvin Minsky, MIT professor and father of artificial intelligence: "Anthropic pays engineers $900K to build multi-agent AI systems. The blueprint is 40 years old, from an MIT professor who proved intelligence is just a swarm of dumb specialists." the thread above shows you how to turn one AI into a team of specialized agents, each with its own job and memory, all managed by a boss. brilliant. it is also marvin minsky's 1986 theory of how your own mind works. minsky's whole idea was that intelligence is not one smart thing. it is a society of tiny, mindless agents, each doing a single dumb job, none of them intelligent alone. put enough of them together under a few managers and intelligence emerges. that is not a metaphor for the claude trick. it is the claude trick. so when you spin up specialized sub-agents and delegate, you are not inventing a new hack. you are rebuilding the architecture minsky described forty years ago, the same one your brain has run your entire life. he co-founded the field, taught it at MIT, and left it all in this free lecture. same story i keep telling: the "new" AI trick is usually an old idea in a new wrapper. here is the part the thread skips, and minsky knew it. a society of agents is only as good as how you organize it. one dumb specialist is useless. a thousand, badly managed, is chaos. the edge was never spawning the agents. it is the orchestration, knowing which specialist to call, when, and how to combine their answers. the tool is free. the judgment is the whole game.

Rossst.03

145,393 views • 4 days ago

D.R. Congo 🇨🇩 - Rwanda 🇷🇼 and M23. . UNCENSORED MINI PODCAST . In this video, I address something I find completely ridiculous, disturbing, and deeply revealing. Instead of confronting the brutal reality that over 20 million Congolese have lost their lives in the last 30 years due to Rwanda’s military aggression, proxy wars, and destabilization in Eastern Democratic Republic of Congo, many Rwandese—and their defenders—would rather debate whether Congolese people “sound hateful” when we speak out. Think about that. Not the mass graves. Not the displaced millions. Not the looted minerals. Not the armed groups. Not the endless war imposed on Eastern Congo. But tone. This video calls out the hypocrisy of shifting the conversation away from human lives and toward comfort, optics, and emotional policing. When a people have been brutalized for decades, when entire regions are trapped in perpetual war, when children are born into violence and never see peace, outrage is not hatred—it is a natural human response. You cannot lecture victims about language while ignoring their suffering. You cannot cry “hate speech” while staying silent about mass death. You cannot demand politeness from people whose families were erased. This is not about insults. This is not about ethnicity. This is about priorities. If the discussion does not begin with why Eastern Congo has been turned into a killing field, then it is a dishonest discussion. If millions of Congolese lives are treated as a footnote while narratives and reputations are protected, then the moral compass is broken. This video is a reminder that Congolese pain is not a debate topic. Our deaths are not a public relations issue. And our anger does not need approval. Before asking how we speak, ask why we are bleeding. ▶️ Watch. Reflect. Share. ✊🏾 Justice before comfort. Truth before optics. Congo before narratives. . #congo #congolese #rwanda #rwandan #africa #drc #rdcongo #rdc #NationalPrayerBreakfast #nationalprayerday #felixtshisekedi #PaulKagame

Patrick Rules

14,752 views • 5 months ago

Predator teacher caught and arrested trying to hook up with 14 year old. ​If you think you’ve heard every excuse in the book, wait until you meet Levi Sholin. ​On September 18, 2025, the 64-year-old homeschool teacher made a 70-mile trek from Miami to Delray Beach. He wasn't there for a lesson plan or a field trip—he was there to meet a 14-year-old boy he’d been grooming online. ​Instead of a teenager, Sholin walked into a Target parking lot and straight into a Delray Beach Police Department sting operation. ​In this bodycam footage, the entitlement is staggering. After traveling over an hour to exploit a child, Sholin was not arrested at the scene as he was allowed to turn himself in at the police department. When the time came to turn himself in Sholin's primary concern is... the clock. As the officers move in to execute the warrant, Sholin begins a lecture on religious timing. He repeatedly argues that because it’s Friday afternoon and the Sabbath is approaching, the police are "trapping" him by making the arrest now. ​"You’re making a big, big mistake... I was brought here under false pretenses." ​He actually expected the police to let him go home for the holy day and "turn himself in" later. Sorry, Levi—the law doesn't pause for your weekend plans when you’re facing felony charges. ​The Charges: Sholin was hit with felony counts of Traveling to Meet a Minor and Solicitation. ​The Betrayal: As a homeschool teacher, he held a position of ultimate trust. This case highlights the terrifying "wolf in sheep’s clothing" reality of predators who use professional credentials to mask their true intent. ​The Current Status: Sholin is currently awaiting trial in Palm Beach County. He’s out on bond with a GPS monitor—meaning his "Sabbath" is now spent under the watchful eye of the state.
4:21

Sensitive content

Predator teacher caught and arrested trying to hook up with 14 year old. ​If you think you’ve heard every excuse in the book, wait until you meet Levi Sholin. ​On September 18, 2025, the 64-year-old homeschool teacher made a 70-mile trek from Miami to Delray Beach. He wasn't there for a lesson plan or a field trip—he was there to meet a 14-year-old boy he’d been grooming online. ​Instead of a teenager, Sholin walked into a Target parking lot and straight into a Delray Beach Police Department sting operation. ​In this bodycam footage, the entitlement is staggering. After traveling over an hour to exploit a child, Sholin was not arrested at the scene as he was allowed to turn himself in at the police department. When the time came to turn himself in Sholin's primary concern is... the clock. As the officers move in to execute the warrant, Sholin begins a lecture on religious timing. He repeatedly argues that because it’s Friday afternoon and the Sabbath is approaching, the police are "trapping" him by making the arrest now. ​"You’re making a big, big mistake... I was brought here under false pretenses." ​He actually expected the police to let him go home for the holy day and "turn himself in" later. Sorry, Levi—the law doesn't pause for your weekend plans when you’re facing felony charges. ​The Charges: Sholin was hit with felony counts of Traveling to Meet a Minor and Solicitation. ​The Betrayal: As a homeschool teacher, he held a position of ultimate trust. This case highlights the terrifying "wolf in sheep’s clothing" reality of predators who use professional credentials to mask their true intent. ​The Current Status: Sholin is currently awaiting trial in Palm Beach County. He’s out on bond with a GPS monitor—meaning his "Sabbath" is now spent under the watchful eye of the state.

Giggling Ganon

51,572 views • 2 months ago

I was watching a lecture on YouTube by David Tse from Stanford (see link below), and he quoted his advisor Bob Gallager: "Good theory should prune rather than grow the knowledge tree." To demonstrate what Gallager meant, we takes one idea… "how do you move mass from one shape to another as cheaply as possible?" and watch 200 years of mathematics cut that idea down to its clean geometric core. Monge (1781, "Mémoire sur la théorie des déblais et des remblais") A mound on the left, a trench on the right, and every grain of earth has to choose one destination and stick to it. In the animation that’s strict one-to-one matching: each point marches to a single partner, no splitting, no sharing. It’s beautiful but rigid and hard to work with. Kantorovich (1942, "On the Translocation of Masses"; 1948, "On a Problem of Monge") Kantorovich relaxes the rules. Instead of forcing each point to pick exactly one target, he allows mass to split: part of it can go here, part of it can go there. On screen you see packets of mass spraying from one source cell to several targets along smooth arcs. The problem becomes a clean convex optimisation problem. Brenier (1991, "Polar factorization and monotone rearrangement of vector-valued functions"); McCann (1995, "Existence and uniqueness of monotone measure-preserving maps"; 1997, "A convexity principle for interacting gases") Bernier then shows that, in the most natural cost setting, the best way to move mass always comes from a hidden height function whose slopes tell you where to send things. McCann shows that many natural energy functionals behave nicely along the paths generated this way. In the animation you see contour lines of that hidden landscape, with particles gliding along the most economical path between the two shapes. Jordan-Kinderlehrer-Otto (1998, "The variational formulation of the Fokker-Planck equation") JKO change the question from "what is the best single shuffle?" to "how does a whole cloud evolve in time?". They show that a familiar diffusion-with-drift equation can be reinterpreted as steepest descent of a free energy in the space of probability distributions. In the scene, a blob both smooths out and gets pulled toward the embankment while a free-energy counter steadily drops. Ambrosio-Gagli-Savaré (2005, "Gradient flows in metric spaces and in the spaces of probability measures") Ambrosio-Gigli-Savaré take that idea and generalise it far beyond this one setting. They build a theory of gradient flows on abstract metric spaces: you only need a notion of distance and an energy, and you can talk about curves of maximal slope. Wasserstein spaces become one important example among many. In the final scene the probability field evolves in the top panel while its energy traces a clean descending curve below.

Mathelirium

65,705 views • 7 months ago

Lecture 2 on our Quantum Mechanics Series Schrödinger’s equation doesn’t start from mystery. It starts from a very specific bet…the state of a particle is a complex field ψ(x,t), and whatever dynamics we write down must move ψ forward in time in a way that preserves total probability. We ask a basic question…what equation should ψ satisfy so that |ψ|² behaves like a conserved density, the way mass density does in fluid flow? What is ψ? Think of ψ(x,t) as the amplitude assigned to “the particle is at position x at time t”. It’s not a probability. It’s the object you add first, and only at the end do you square p(x,t) = |ψ(x,t)|² Because ψ is complex, it has magnitude and phase. Write it in polar form ψ(x,t) = r(x,t) exp(i θ(x,t)) Then r² = |ψ|² is the density, and θ will end up controlling flow (the probability current). Where does Schrödinger’s equation come from? Start with two empirical inputs about waves and particles: E = ħ ω p = ħ k Here ħ (“h-bar”) is Planck’s constant divided by 2π. It’s the unit conversion factor between the wave description (frequency ω, wavevector k) and the particle description (energy E, momentum p). In units, ħ has units of joule-seconds, so multiplying ω (1/seconds) gives energy (joules), and multiplying k (1/meters) gives momentum (kg·m/s). It’s the number that tells you how much energy or momentum you get per unit frequency or wavenumber. A plane wave with angular frequency ω and wavevector k is ψ(x,t) = A exp(i(k·x − ω t)) Now notice what derivatives do to this wave: ∂ψ/∂t = −i ω ψ ∇ψ = i k ψ ∇²ψ = −|k|² ψ Multiply those identities by ħ: i ħ ∂ψ/∂t = ħ ω ψ = E ψ −i ħ ∇ψ = ħ k ψ = p ψ −ħ² ∇²ψ = ħ² |k|² ψ = p² ψ So for plane waves, the operators Ê = i ħ ∂/∂t p̂ = −i ħ ∇ act like energy and momentum! Now use the classical nonrelativistic energy relation: E = p²/(2m) + V(x) This is bookkeeping for a particle moving slow enough that relativity can be ignored. The term p²/(2m) is kinetic energy. If p = mv, then p²/(2m) = (m²v²)/(2m) = (1/2)mv². The term V(x) is potential energy. It depends on position because forces come from spatially varying energy. A slope in V pushes the particle. Examples: for a charged particle in an electric potential φ(x), V(x) = q φ(x). Near Earth, V(z) = mgz. The point is total energy equals kinetic plus potential. Turn that into an equation for ψ by replacing E and p with the operators above: Ê ψ = (p̂²/(2m) + V) ψ Compute p̂² = (−i ħ ∇)·(−i ħ ∇) = −ħ² ∇², so we get i ħ ∂ψ/∂t = ( −ħ²/(2m) ∇² + V(x) ) ψ That is the time-dependent Schrödinger equation. The derivation here is a controlled heuristic: we matched the plane-wave identities to the measured relations E = ħω and p = ħk, then imposed the same energy bookkeeping as classical mechanics. Why this equation is the right kind of rule If ψ is the state, we need a rule that preserves total probability: ∫ |ψ(x,t)|² dx = 1 Schrödinger evolution does. You can see it by deriving a continuity equation. Let ρ(x,t) = |ψ|² = ψ* ψ. Take a time derivative: ∂ρ/∂t = ψ* ∂ψ/∂t + ψ ∂ψ*/∂t Use Schrödinger and its complex conjugate: ∂ψ/∂t = (1/(i ħ)) ( −ħ²/(2m) ∇²ψ + Vψ ) ∂ψ*/∂t = (−1/(i ħ)) ( −ħ²/(2m) ∇²ψ* + Vψ* ) Plug in. The V terms cancel exactly, and what remains can be rearranged into a divergence: ∂ρ/∂t + ∇·j = 0 where the probability current is j = (ħ/(2mi)) ( ψ* ∇ψ − ψ ∇ψ* ) This is the best way to explain ehat ψ is: |ψ|² behaves like a conserved density, and the phase of ψ is what drives the current j. So in this series, ψ isn’t a slogan. It’s the object whose modulus squared is the density, whose phase generates flow, and whose time evolution is fixed (up to V) by matching wave relations to energy bookkeeping: i ħ ∂ψ/∂t = ( -ħ²/(2m) ∇² + V ) ψ #QuantumMechanics #SchrodingerEquation #WaveFunction #BornRule #Physics #MathematicalPhysics

Mathelirium

40,835 views • 6 months ago