We optimized the topology of electrodes in the composite piezoresistive sensors for highest sensitivity and lowest power consumption. We used multi-fidelity Finite Element simulations, built deep learning reduced order models, and examined various electrode configurations to find the optimal design.
Read MoreThe polymer-based conductive composites have shown great potential for sensing applications because of their high flexibility and sensitivity. We developed a numerical model based on the Finite Element Method, which offers micro- and macro-level analyses from mechanical to electrostatic predictions.
Read MoreWe describe an approach to find optimized ANN architectures for building metamodels using the Keras library with TensorFlow as the backend. We use the Hyperband algorithm to optimize the hyperparameters of our model on top of a brute-force tuning by a variable-batch training.
Read MoreBy transferring the learning from a metamodel that was already trained to predict the heat transfer rate for natural convection flows in enclosures, we construct a deep neural network to account for enclosures with a centered hole. We demonstrate that a transfer learning approach reduces the computational and training costs.
Read MoreWe develop a framework for transfer learning with deep neural networks for natural convection. We consider the benchmark problem of an air-filled enclosure and train our network to predict the heat transfer rate for different Rayleigh inputs. We then transfer the learning to consider enclosures filled with an arbitrary fluid.
Read MoreThe numerical simulations using coarse grid systems may provide precise results for a limited range of input parameters. As such, we utilize a multi-grid dataset for training an ANN in order to reduce simulation times. We denoised the dataset, and retrained the ANN based on abnormalities observed in training losses.
Read MoreengML.com aims to share my research with others who are interested in leveraging ML to improve engineering analyses.