data-driven algorithms. Most of these approaches have, however, been restricted to linear material properties such as the effective elastic stiffness in three dimensions7,8or Poisson’s ratio9. Extensions to no
hierarchical parallel evolutionary algorithmsmulti objectivegame theoryPareto front and Nash equilbriumIt is shown that solutions of many model non-linear equations of the hydrodynamic type (including the Navier-Stokes equations in the theory of a viscous fluid) in the form of localized wave ...
Recently, diffusion models have been proven to perform remarkably well in text-to-image synthesis tasks in a number of studies, immediately presenting new
Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023). CAS PubMed Google Scholar Eberhardt, J., Santos-Martins, D., Tillack, A. F. & Forli, S. AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python ...
These four algorithms use different evolutionary criteria. The optimality tolerance is set to 0.001. The lower and upper bounds for the control variable ξ are set to 0.001 and 0.999, respectively. Different initial values V0 of the variable ξ are tested in each case, and the results are ...
are shown in Figs.2E and F, respectively. Although the binding pose accuracy decreased as the ligand size increased, our models performed better for larger ligands than the baseline methods (Fig.2E), indicating their ability to efficiently handle many degrees of freedom. Besides, our models were...
Pharmacokinetic (PK) models are used to extract physiological information from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) sequences. Some of the most common models employed in clinical practice, such as the standard Tofts model (STM) or the extended Tofts model (ETM), do not...
- evolutionary graph theory - epidemic models on graphs - data-driven approaches - temporal networks - seeding strategies - optimization of dynamic processes in networks === IMPORTANT DATES === Submission deadline: 9 August 2015 First round decision: end of November 2015 ...
PyFRAP's implementation of the AIC allows users to compare the models mentioned above and determines the most likely model based on a relative weighted measure that includes both the model's log-likelihood and its degrees of freedom, i.e., the number of model parameters. Moreover, PyFRAP ...
in application areas ranging from engineering to finance. On the other hand, PINN models have also been extended to backward problems, such as advection-dispersion equations55, stochastic problems56, flow problems57, and conservation laws58. In these backward problems, the training data are inputted...