Scientific Computing

Accelerate Scientific Computing with AAD

PDEs, optimisation and regression 6 to 100x faster, with exact adjoint derivatives. Less development time and lower run costs for large scientific calculations.

Focus on Science, Not Optimisation

Our software allows scientists and engineers to focus on mathematical problems rather than performance optimisation, achieving better performance and calculating adjoints automatically without manual derivative coding.

6-100x Faster

Than manual adjoint implementation or finite differences

Automatic Gradients

No hand-coding required for derivatives

Million+ DOF

Handle massive finite difference/element grids efficiently

Core Capabilities

Gradient-Based Optimisation

Automatically calculate the full Jacobian at a fraction of the usual computational cost.

Large-Scale PDE Sensitivity

MatLogica's AADC processes extremely large finite difference PDE schemes efficiently. It executes back-propagation quickly and flawlessly.

Automatic Adjoint Generation

No manual derivative coding required. AADC automatically generates efficient adjoint code for your PDE solvers, optimisation routines.

Applications Across Scientific Domains

Geophysics & Seismology

  • Full Waveform Inversion (FWI): Compute gradients for velocity model updates
  • Seismic Imaging: Sensitivity analysis for subsurface parameters
  • Reservoir Simulation: History matching and parameter estimation
  • Gravity/Magnetic Inversion: Fast gradient computation for geophysical inversions

Computational Fluid Dynamics

  • Shape Optimisation: Aerodynamic design with gradient-based methods
  • Adjoint Flow Solvers: Navier-Stokes sensitivity analysis
  • Heat Transfer: Thermal optimisation and inverse problems
  • Multiphase Flow: Parameter identification in complex flows

Weather & Climate Modelling

  • 4D-Var Data Assimilation: Efficient gradient computation for atmospheric models
  • Weather Forecasting: Adjoint sensitivity for initial conditions
  • Ocean Modelling: Parameter estimation in circulation models
  • Climate Sensitivity: Compute sensitivities to forcing parameters

Medical & Biomedical

  • Electric Impedance Tomography (EIT): Fast inverse problem solution
  • MRI Reconstruction: Gradient-based image optimisation
  • Diffusion Imaging: Parameter estimation in tissue models
  • Biomechanics: Material property identification

Electromagnetics

  • Antenna Design: Shape optimisation using adjoint methods
  • Scattering Problems: Inverse electromagnetic problems
  • Photonics: Device optimisation with Maxwell solvers
  • Radar Imaging: Gradient-based reconstruction

Materials & Structures

  • Topology Optimisation: Structural design with gradients
  • Material Identification: Inverse problems in mechanics
  • Composite Design: Multi-scale optimisation
  • Fracture Mechanics: Parameter sensitivity analysis

Accelerate Your Scientific Computing

Let us benchmark AADC on your PDE solver or optimisation problem