Function <i>χ</i>_<i>opt</i> superimposed with the phase-field function indicating the channel geometry. by Martina Bukač (20161460)

The phase-field function for three different network configurations considered in this work. by Martina Bukač (20161460)

Results of proposed algorithms and algorithms in [9] for 18 instances. by Jin Zhang (53297)

<b>Opti2Phase</b>: Python scripts for two-stage focal reducer by Morgan Najera (21540776)

“…</p><p dir="ltr">The package includes:</p><ul><li>Scripts for first-order analysis, third-order modeling, optimization using a Physically Grounded Merit Function (PGMF), and RMS-based refinement.</li><li>A subfolder named <b>Images</b>, which stores the figures generated by six of the seven provided scripts.…”

Illustration of methods based on Green’s function first-passage algorithms. by Jean Marc Tregan (15195173)

“…<p>Illustration of methods based on Green’s function first-passage algorithms.…”

Multi-functional liquid crystal devices based on random binary matrix algorithm by Tibin Zeng (10890697)

“…Here, we propose a new phase mixing algorithm based on the random binary matrix and describe the theoretical foundation and experimental realisation of the proposed scheme. …”

TV-BayesOpt algorithm performance for tracking a gradual drift in the optimal stimulation phase for phase-locked stimulation, <i>ψ</i>*. by John E. Fleming (8533956)

“…For each estimated GPR the confidence bounds observed at the predicted optimal phase value are small and become larger for values further away from this value due to the algorithm’s acquisition function prioritizing exploitation of the parameter space during the optimization process.…”

TV-BayesOpt algorithm performance for tracking a periodic drift in the optimal stimulation phase for phase-locked stimulation, <i>ψ</i>*. by John E. Fleming (8533956)

“…For each estimated GPR the confidence bounds observed at the predicted optimal phase value are small and become larger for values further away from this value due to the algorithm’s acquisition function prioritizing exploitation of the parameter space during the optimization process.…”

Function graph and algorithm iterative graph. by WanRu Zhao (18980374)

Subjects:

Loss function values of target detection algorithms during training. by Guodong Su (7876934)

Diagram of the algorithm used during the training phase. by Ana P. Arantes (17131753)

TV-BayesOpt algorithm performance for tracking a superimposed (gradual and periodic) drift in the optimal stimulation phase for phase-locked stimulation, <i>ψ</i>*. by John E. Fleming (8533956)

“…For each estimated GPR the confidence bounds observed at the predicted optimal phase value are small and become larger for values further away from this value due to the algorithm’s acquisition function prioritizing exploitation of the parameter space during the optimization process.…”

Route for bays29 output by ABSQL algorithm. by Jin Zhang (53297)

“…DSRABSQL builds upon the Q-learning (QL) algorithm. Considering its problems of slow convergence and low accuracy, four strategies within the QL framework are designed first: the weighting function-based reward matrix, the power function-based initial Q-table, a self-adaptive <i>ε-beam</i> search strategy, and a new Q-value update formula. …”

Simulation parameters for the QL, ABSQL and DSRABSQL algorithms. by Jin Zhang (53297)

Limitation of the conventional DBT reconstruction algorithm. by Yunsu Choi (11993484)

The optimal route of Berlin52 output by DSRABSQL algorithm. by Jin Zhang (53297)

The optimal route of Tsp225 output by DSRABSQL algorithm. by Jin Zhang (53297)

The optimal route of Berlin52 output by ABSQL algorithm. by Jin Zhang (53297)

The optimal route of Tsp225 output by ABSQL algorithm. by Jin Zhang (53297)

Flowchart of the phase optimization algorithm. by Maxim Terekhov (3429614)

“…<p>At stage II, phase vectors {Φ^top} providing cost function values F_c above 90% of maximum are selected as initial condition {Φ₀} for iterative NLS-search and selection of the optimum vectors {Φ^opt}. …”