The output layer employs the Sigmoid activation function to produce a probability score. Both networks are trained using the Binary Cross-Entropy loss function (PyTorch 2.3.0 BCELoss function). The training process employs the Adam optimizer with a learning rate of 0.0001, a batch size of 16, ...
We use a stack-augmented recurrent neural network (stack-RNN) as the generative model trained with cross-entropy loss function minimization and the REINFORCE algorithm to conduct policy gradient updates during learning110,111. The stack-RNN architecture is particularly suited for sequence prediction ...
Such methods are often optimized to reduce the mean squared error (MSE) or binary cross entropy between the output and a training dataset of optimized designs. While convenient, we show that this choice may be myopic. Specifically, we compare two methods of optimizing the hyperparameters of ...
The inverse network structure is composed of two fully connected layers with the Sigmoid and ReLU activation functions, followed by two 2D deconvolution layers, as illustrated in Fig.6.The NN is trained to forecast dielectric vias to minimize binary cross-entropy (BCE) loss, As follows29: $$\...
Understanding the mechanisms of deformation of biological materials is important for improved diagnosis and therapy, fundamental investigations in mechanobiology, and applications in tissue engineering. Here we demonstrate the essential role of interstit
If it is required to find x such that only the data Kx = y is used, the information subject to the data needs to be minimized, that is, the entropy has to be maximized. The mathematical justification for the choice L(x) = -ℰ MathType@MTEF@5@5@+=feaagaart1ev2aaatCvAUfKttLear...
Encoding the n × n combination of building blocks into a numerical representation (for example, a binary representation of two building blocks is used), we then employ a DNN in conjunction with a genetic algorithm (GA) to find optimal combinations, based on the previous fitness function....
We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an
In order to solve the inverse kinematics (IK) of complex manipulators efficiently, a hybrid equilibrium optimizer slime mould algorithm (EOSMA) is proposed. Firstly, the concentration update operator of the equilibrium optimizer is used to guide the anis
Concurrence, negativity and linear entropy. (a) Top: Concurrence versus inter-emitter distance for dielectric structures obtained by setting the concurrence (orange) and the negativity (blue) as the optimization function (P/γ = 5 ⋅ 10−3). Bottom: Negativity versus distance for the same de...