Speed up PDEs, optimization methods, and regressions by 6-100x using state-of-the-art automatic adjoint differentiation. With MatLogica, you save development time and reduce execution costs for large-scale scientific applications.
Our software allows scientists and engineers to focus on mathematical problems rather than performance optimization, achieving better performance and calculating adjoints automatically without manual derivative coding.
Than manual adjoint implementation or finite differences
No hand-coding required for derivatives
Handle massive finite difference/element grids efficiently
Automatically calculate the full Jacobian at a fraction of the usual computational cost. Essential for inverse problems, parameter estimation, and large-scale optimization in scientific computing.
MatLogica's AADC processes extremely large finite difference PDE schemes efficiently. It executes back-propagation quickly and flawlessly, computing sensitivities of PDE solutions relative to coefficients, boundary conditions, or initial data.
No manual derivative coding required. AADC automatically generates efficient adjoint code for your PDE solvers, optimization routines, and regression models.
Let us benchmark AADC on your PDE solver or optimization problem