Stochastic Nature of Overbank Flow Turbulence in Straight Compound Channels with Vegetated Floodplains

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs

Solving partial differential equations (PDEs) is a central task in scientific computing. Recently, neural network approximation of PDEs has received increasing attention due to its flexible meshless discretization and its potential for high-dimensional problems. One fundamental numerical difficulty is that random samples in the training set introduce statistical errors into the discretization of loss functional which may become the dominant error in the final approximation, and therefore overshadow the modeling capability of the neural network. In this work, we propose a new minmax formulation to optimize simultaneously the approximate solution, given by a neural network model, and the random samples in the training set, provided by a deep generative model. The key idea is to use a deep generative model to adjust random samples in the training set such that the residual induced by the approximate PDE solution can maintain a smooth profile when it is being minimized. Such an idea is achieved by implicitly embedding the Wasserstein distance between the residual-induced distribution and the uniform distribution into the loss, which is then minimized together with the residual. A nearly uniform residual profile means that its variance is small for any normalized weight function such that the Monte Carlo approximation error of the loss functional is reduced significantly for a certain sample size. The adversarial adaptive sampling (AAS) approach proposed in this work is the first attempt to formulate two essential components, minimizing the residual and seeking the optimal training set, into one minmax objective functional for the neural network approximation of PDEs

Mechanical behaviors of hydrogel-impregnated sand

Hydrogel has been widely used in medical studies due to their unique integration of solid and liquid properties. There is limited studies of using hydrogel in construction materials. The goal of this study was to investigate the effect of hydrogel on mechanical behaviors of sandy materials. The effects of reaction time, sodium alginate content, and curing temperature on mechanical behaviors of hydrogel-impregnated sand were studied through unconfined compression tests, falling head permeability tests, consolidated and undrained triaxial tests, scanning electron microscopy, and durability tests. The unconfined compression strength (UCS) increased with sodium alginate content, but the hydraulic conductivity of hydrogel-impregnated sand decreased with sodium alginate content. The optimum reaction time and curing temperature were found to be 3 days and 50 °C, respectively, for the hydrogel-impregnated sand. The stress-strain curves of hydrogel-impregnated sand indicated that the ductility of hydrogel-impregnated sand was significantly improved compared with the traditional cementitious method. Moreover, the results of durability tests indicated that approximately 60% of the original UCS of hydrogel-impregnated sand still remained after 12 wet-dry and freeze-thaw cycles

Bayesian Nonlinear Tensor Regression with Functional Fused Elastic Net Prior

Tensor regression methods have been widely used to predict a scalar response from covariates in the form of a multiway array. In many applications, the regions of tensor covariates used for prediction are often spatially connected with unknown shapes and discontinuous jumps on the boundaries. Moreover, the relationship between the response and the tensor covariates can be nonlinear. In this article, we develop a nonlinear Bayesian tensor additive regression model to accommodate such spatial structure. A functional fused elastic net prior is proposed over the additive component functions to comprehensively model the nonlinearity and spatial smoothness, detect the discontinuous jumps, and simultaneously identify the active regions. The great flexibility and interpretability of the proposed method against the alternatives are demonstrated by a simulation study and an analysis on facial feature data

Multi-objective analysis of the co-mitigation of CO2 and PM2.5 pollution by China's iron and steel industry

China has experienced serious fine particulate matter (PM2.5) pollution in recent years, and carbon dioxide (CO2) emissions must be controlled so that China can keep its pledge to reduce CO2 emissions by 2030. The iron and steel industry is energy intensive and contributes significantly to PM2.5 pollution in China. The simultaneous reduction of CO2 emissions and PM2.5 pollution while minimizing the total mitigation costs remains a crucial issue that must be resolved. Using a multi-objective analysis, we compared potential technology combinations based on various policy preferences and targets. Our results showed that policies designed to mitigate PM2.5 pollution have substantial co-benefits for CO2 emissions reductions. However, policies focused solely on reducing CO2 emissions fail to effectively reduce PM2.5. Furthermore, CO2 emissions reductions correspond to large financial costs, whereas PM2.5 pollution reductions are less expensive. Our results suggest that under limited budgets, decision makers should prioritize PM2.5 reductions because CO2 reductions may be simultaneously achieved. Achieving large decreases in CO2 emissions will require further technological innovations to reduce the cost threshold. Thus, China should focus on reducing PM pollution in the short term and prepare for the expected challenges associated with CO2 reductions in the future