Physics problems solved
step by step
Enter any mechanics, electromagnetism, or thermodynamics problem and get a full derivation — from raw variables to verified final answer — with every law cited.
| Mass (m) | 2 kg |
| Angle (θ) | 30° |
| Height (h) | 5 m |
| Friction | None (frictionless) |
| Initial velocity | 0 m/s (released from rest) |
| Gravity (g) | 9.81 m/s² |
All inputs normalized to SI (MKS) units before processing.
Conservation of Mechanical Energy
Since the surface is frictionless, mechanical energy is conserved. All gravitational potential energy converts to kinetic energy:
The mass cancels, meaning the result is independent of m — only height and gravity determine final velocity.
How the solver works
Describe your problem
Type the problem in plain English or upload a photo of your textbook page. The engine reads both text prompts and handwritten equations.
AI selects the right laws
A neuro-symbolic pipeline identifies which physical laws apply — Newton’s Second Law, conservation of energy, Kirchhoff’s rules — and builds the derivation chain.
Review the full derivation
Every algebraic step is shown with the law cited. SI unit verification runs automatically. You get a logically complete solution, not just a number.
What Physics AI Solver covers
Classical Mechanics
Kinematics, projectile motion, Newtonian dynamics, rotational motion, and fluid statics — including inclined planes and pulley systems.
Electromagnetism
Circuit analysis with Kirchhoff’s Laws, Coulomb’s Law, Biot-Savart, Faraday’s induction — and RC/RLC transient analysis.
Thermodynamics
First and Second Law applications, Carnot cycle efficiency, PV diagrams, ideal gas state changes, and heat transfer calculations.
Modern Physics
Special relativity (time dilation, length contraction), photoelectric effect, de Broglie wavelength, radioactive decay, and nuclear binding energy.
Physics AI Solver vs. alternatives
| Tool | Step-by-step derivation | Law cited per step | Image upload | Units verified | Free tier |
|---|---|---|---|---|---|
| Physics AI Solver | ✓ Full | ✓ Yes | ✓ Yes | ✓ Automatic | ✓ Yes |
| Wolfram Alpha | Partial | ✗ | ✗ | ✓ | Limited |
| Chegg | ✓ | Sometimes | ✓ | ✗ | ✗ Paywall |
| ChatGPT | Inconsistent | Partial | ✓ (GPT) | ✗ | Limited |
| Photomath | ✓ | ✗ | ✓ | ✗ | Math only |
What is a Physics AI Solver?
A physics AI solver is a computational tool that accepts a natural-language description of a physics problem — or a photograph of one — and returns a structured, step-by-step solution. Unlike a standard calculator that requires a formula to be pre-entered, a physics AI solver identifies which formula applies before performing any arithmetic. The distinction matters enormously in practice: students and engineers often know the numbers but are unsure which law governs the situation.
Physics AI Solver combines two approaches that have historically been kept separate. A neural language model parses the prose of your problem, extracting quantities, units, and physical context. That parsed representation is then handed to a symbolic mathematics engine — deterministic software that cannot hallucinate a wrong coefficient. The result is accuracy that pure LLM-based tools cannot reliably achieve.
How to use Physics AI Solver
- Choose a physics domain. Select Mechanics, Electromagnetism, Thermodynamics, or Modern Physics from the tab row. This primes the parser for the relevant vocabulary and formula set.
- Type or paste your problem. Write it as naturally as you would in a homework assignment. Include all given quantities with units. If you have a diagram, upload the image instead.
- Click “Solve Problem.” The solver extracts variables, selects applicable laws, and builds a derivation chain. The six-step processing animation reflects real computation stages, not decoration.
- Review Given Data and Method tabs first. These confirm the parser read your problem correctly before you proceed to the full derivation.
- Access the full step-by-step solution. The complete derivation — including intermediate substitutions, unit cancellations, and the verified final answer — is available via the full solution link.
Who uses a physics AI solver?
The most frequent users of AI-powered physics problem solvers are undergraduate students in STEM programmes. Introductory mechanics and electromagnetism courses assign large problem sets with tight deadlines; a tool that explains the method, not just the answer, helps students learn the reasoning behind each step rather than copying a result.
High school students preparing for AP Physics represent a second large segment. AP Physics C in particular demands mastery of calculus-based mechanics and electromagnetism — territory where standard arithmetic solvers fall short. Physics AI Solver handles derivatives and integrals embedded in physics derivations, making it useful for topics such as rotational inertia integrals and RC circuit transient analysis.
A third group — self-taught learners and career changers entering engineering — use the tool to bridge gaps in formal physics education. These users typically have strong mathematical ability but lack the pattern recognition that tells an experienced physicist which law applies. The “Applicable Law” tab addresses this directly by naming the governing principle before the algebra begins.
Mechanics problems: what the solver handles
Classical mechanics remains the most commonly solved domain. Within kinematics, the solver handles one-dimensional uniform acceleration, two-dimensional projectile motion (with and without initial height), and circular motion problems including centripetal acceleration. For dynamics, it applies Newton’s Second Law to systems with multiple forces — friction (both static and kinetic), tension, normal force, and applied force vectors on inclined planes.
Energy methods — conservation of mechanical energy, the work-energy theorem, and power — are handled symbolically, with the solver correctly identifying when non-conservative forces (friction) are present and switching to a work-energy formulation rather than simple energy conservation. Rotational mechanics problems — torque, moment of inertia for standard geometries, angular momentum conservation — are fully supported.
Electromagnetism and circuit analysis
Circuit problems form a large share of electromagnetism queries. The solver automatically applies Kirchhoff’s Current Law (KCL) and Kirchhoff’s Voltage Law (KVL) to resistor networks, identifying series and parallel configurations. For capacitor and inductor circuits, it performs transient analysis using the characteristic time constant τ = RC or τ = L/R.
Electrostatics problems — point charges, Gauss’s Law applications, and electric potential calculations — are solved with correct unit handling throughout. Magnetism queries invoking the Biot-Savart Law (magnetic field from a current element) or the Lorentz force (force on a moving charge) are parsed from natural language without requiring the user to know the formula name in advance.
Thermodynamics and heat engines
The thermodynamics module covers the standard undergraduate curriculum: First Law applications (internal energy, heat, and work), entropy calculations using ΔS = Q/T for reversible processes, and Carnot efficiency as a function of source and sink temperatures. Ideal gas problems — isothermal, adiabatic, isobaric, and isochoric processes — are solved using PV = nRT with automatic identification of which variable changes and which is held constant.
Heat engine cycle problems involving PV diagrams are a common use case. The solver identifies the cycle type (Carnot, Otto, diesel) from the problem description and computes efficiency, net work output, and heat rejected without requiring the user to sketch the diagram manually.
Why dimensional analysis matters in physics problem solving
Physics has a built-in consistency check that pure algebra lacks: every quantity carries units, and any valid equation must balance dimensionally. When the formula for kinetic energy yields kg·m²/s² rather than kg·m²/s, something in the derivation chain is wrong — even if the arithmetic is correct.
Physics AI Solver runs automatic dimensional analysis at each derivation step. If the intermediate unit structure does not match the expected physical dimension for that quantity, the solver flags the inconsistency and re-derives from the prior step. This eliminates the most common category of physics error: applying the right formula with a wrong exponent or missing a square root.
Physics AI Solver vs. asking ChatGPT
General-purpose language models like ChatGPT can answer physics questions, but with a well-documented weakness: they generate plausible-looking derivations that sometimes contain arithmetic errors or apply the wrong formula for the situation. Because these models produce fluent prose, the errors are not always obvious to a student who is still learning the material.
Physics AI Solver addresses this by routing all arithmetic through a deterministic symbolic engine. The language model’s role is limited to parsing the problem; it never performs the calculation. The symbolic engine does not hallucinate — it either finds a valid derivation or reports that one cannot be constructed from the given inputs. This architecture produces consistent, verifiable results across the range of undergraduate physics problems.
Frequently asked questions
The tool uses a two-stage architecture. The language model only parses the natural language of your problem — it extracts quantities, units, and physical context. All arithmetic and algebra is then performed by a separate symbolic mathematics engine that works deterministically, the same way Wolfram Alpha or Python’s SymPy does. This engine cannot produce a plausible-but-wrong intermediate result because it either finds a valid symbolic path or it stops. The combination removes the hallucination risk present in end-to-end LLM solutions while retaining the ability to understand plain-English problem statements.
Yes. The image upload path supports JPG and PNG files of both printed textbook problems and handwritten notes. The computer vision layer is trained on physics-specific notation — it recognizes free-body diagrams, circuit schematics with standard component symbols, and handwritten variable labels like θ, μ, or ω. Extracted values are shown in the Given Data tab before the derivation begins, so you can verify the parser read your problem correctly before proceeding.
Yes. Physics AI Solver handles calculus-based derivations including integration for rotational inertia of continuous mass distributions, time-derivative relationships in oscillatory motion, and transient circuit analysis using differential equations. The symbolic engine evaluates definite integrals and applies the product and chain rules as needed within a physics context. This makes it suitable for AP Physics C: Mechanics and AP Physics C: Electricity and Magnetism, as well as first and second-year university physics courses.
Before any calculation begins, all input values are converted to SI base units — kilograms, metres, seconds, amperes, kelvin. This normalisation step prevents a common source of error in student work: mixing km/h with m/s, or using centimetres where metres are expected. The derivation runs entirely in SI units. The final answer is then expressed in the appropriate derived unit for that quantity — newtons for force, joules for energy, pascals for pressure — with the full unit derivation shown inline.
Multi-law problems are the norm in upper-level physics. For example, a problem involving a charged particle in a magnetic field on an inclined track requires Lorentz force, Newtonian dynamics, and geometric decomposition simultaneously. The solver builds a dependency graph of required equations before beginning derivation, identifying which quantities must be found first to unlock others. The step sequence in the full solution reflects this ordering — you will see which intermediate result feeds into each subsequent step, rather than a linear list that assumes a single derivation path.
Physics educators use the tool to quickly generate worked examples for class notes, problem sets, and exam preparation materials. Because each step names the governing law explicitly — “Applying Conservation of Angular Momentum,” “Applying Kirchhoff’s Voltage Law to loop 2” — the output can be used directly as a pedagogically structured solution. The Given Data tab also makes it easy to verify that a problem is well-posed before assigning it to students, since the parser will flag missing or contradictory constraints.
The Modern Physics module handles special relativity calculations — time dilation (t = γt₀), length contraction, relativistic momentum, and the full energy-momentum relation E² = (pc)² + (mc²)². For introductory quantum mechanics, it covers the photoelectric effect (E = hf − φ), de Broglie wavelength (λ = h/p), and one-dimensional particle-in-a-box energy levels. Nuclear physics topics include radioactive decay rate equations, half-life calculations, and Q-value (binding energy) for nuclear reactions. Advanced quantum mechanics topics requiring full wave-function solutions beyond the particle-in-a-box are outside the current scope.