PhySolve AI Physics Solver | Python PDE/ODE Neural Network Kit (162-Formula Library)
NowPrice:428,01 kr.
Original Price: 503,50 kr.
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Highlights
AI PHYSICS SOLVER — Ask a Question, AI Solves It
Physics-Informed Neural Network Kit + 162-Formula Library + Question Parser
🔥 This is not a formula sheet. It's a working AI physics engine.
Type a question in plain English:
"a 2 kg mass accelerates at 3 m/s^2, find the force" -> F = 6 N
"pendulum of length 1 m, find the period" -> T = 2.007 s
"electron at 1e6 m/s, de Broglie wavelength?" -> 7.27e-10 m
"1 mol gas at 273 K and 101325 Pa, volume?" -> 22.4 L
The app detects the equation, extracts numbers + units, auto-fills
physical constants (G, h, k_B, c, pi, g...) and solves - instantly
for formulas, or by training a neural network for differential
equations. Particle names work too: "electron" automatically
injects its mass and charge.
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WHAT'S INSIDE
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✅ DASHBOARD APP (dark themed GUI)
• Question bar - plain-English physics questions
• PINN Solver tab - 25 differential equations, live NN training
• Equation Library tab - searchable, category-filtered browser
✅ QUESTION PARSER ENGINE
• Extracts values with 40+ unit types (kg, m/s^2, °C, eV, atm...)
• Auto-fills physical constants from context
• Detects what you're solving for ("find X", "what is Y")
✅ 25 NEURAL-NETWORK-SOLVED EQUATIONS
PDEs: heat, heat+source, advection, advection-diffusion, wave,
damped wave, Burgers, Fisher-KPP, Allen-Cahn, KdV solitons,
Sine-Gordon, Schroedinger, Poisson
ODEs: decay, logistic, harmonic/damped/driven oscillators,
nonlinear pendulum, Van der Pol, Duffing, Airy,
Lotka-Volterra, Lorenz system, SIR epidemic
→ free-form initial conditions: sin(pi*x), 2*sech(x)^2, ...
✅ 162-FORMULA PHYSICS LIBRARY
Mechanics (29) • Electromagnetism (23) • Thermodynamics (16)
Electrostatics (15) • Fluids (14) • Waves & Optics (13)
Oscillations (12) • Quantum (12) • Relativity (12)
Heat (10) • Diffusion (6)
Every entry: formula + variable meanings + SI units +
assumptions + worked example + symbolic solver
✅ VALIDATION & BENCHMARKS
• Heat/wave vs exact solutions (<1% error)
• Burgers shock vs finite-difference reference
• 2D Navier-Stokes vs Kovasznay exact flow
• Speed benchmark: trained NN evaluates ~11M points/second
✅ EXTRAS
• Complete PINN engine source (PyTorch, autograd residuals)
• Step-by-step tutorial - write your own PDE solver
• Windows launchers (START.bat opens the dashboard)
• Terminal wizards for both engines
• Pre-trained model + sample output plots included
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PERFECT FOR
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• Physics & engineering students - homework checking, learning PINNs
• Researchers wanting a clean minimal PINN baseline
• Teachers - every formula ships with a worked example
• Developers building scientific ML / surrogate models
• Anyone curious how AI solves differential equations
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TECH DETAILS
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• Python 3.9+ • PyTorch • NumPy • Matplotlib • SymPy • Tkinter GUI
• Runs on CPU, GPU auto-detected (optional speedup)
• One-click START.bat launcher • Instant ZIP download
• Commercial use allowed - see included license
• No API keys, no subscriptions, works fully offline
This is the same PINN technique behind NVIDIA Modulus and modern
surrogate modeling - packaged so you can run it today.
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