Fig. 10: The non-intrusive reduced basis method for the physics-based machine learning framework. | Communications Physics

Fig. 10: The non-intrusive reduced basis method for the physics-based machine learning framework.

From: Physics-based machine learning toolbox for probing concentration under diffusive regime in microfluidics devices

Fig. 10: The non-intrusive reduced basis method for the physics-based machine learning framework.The alternative text for this image may have been generated using AI.

Black arrows indicate the information flow from an input binary image to an output binary image. The figure shown corresponds to the chip with a rock pattern chip with an input size of 512 × 512 pixels. A similar construction approach is used for a chip with a uniform pattern, with an input size of 256 × 256 pixels. a Trained decoder from the Convolutional Autoencoder (CAE) model, used to augment existing segmented images for the construction of the non-intrusive reduced basis (NI-RB) model. b Trained encoder from the CAE model, used to reduce the dimensionality of input images. c Combined latent space of the CAE model and time. d Reduced basis coefficients obtained via Proper Orthogonal Decomposition (POD) on the Lattice–Boltzmann solutions. e Basis functions obtained via POD on the Lattice–Boltzmann solutions.

Back to article page