JMax.
The math-native programming language.
Write math, see the result, and measure the energy it took. One environment for symbolic algebra, simulation, and visualization, compiled to run anywhere and metered in joules. Energy-optimized scientific computing for the AI and compute era.
- > ∂u/∂t = α ∇²u · heat on a plate
- ✓ differentiate x² · sin x symbolic 0.000 J
- ✓ assemble FEM operator sparse 0.02 J
- ↳ solve A·u = b (CG) numeric 0.31 J
- ✓ render field → SVG viz 0.001 J
Math in. Result, plot, and joules out.
The language
Symbolic, numeric, and seen
One environment from the equation to the simulation to the plot.
Math-native syntax
Write mathematics directly. A computer algebra system simplifies and differentiates by construction, so symbolic work stays exact and nearly free.
Simulation built in
Native FEM and PDE solvers with sparse linear algebra. Take a model from equation to result without leaving the language.
Visualization is the output
Results render to vector graphics and WebGPU from one scene description. Seeing the answer is part of computing it.
The discipline
Scientific computing, on a SWaP-2C budget
Simulation and data science have always burned compute without counting it. JMax puts scientific computing on a SWaP-2C budget, key to unlocking the AI and compute revolution.
Size
One environment instead of a stack of separate tools.
Weight
No glue code or data copies between symbolic, numeric, and plotting tools.
Power
Every computation metered in joules, symbolic to solve.
Cost
Cheap symbolic steps stay cheap; you pay for the solve, not the setup.
Cooling
Less wasted computation means less heat per result.