Estimating the governing equation parameter values is essential for integrating experimental data with scientific theory to understand, validate, and predict the dynamics of complex systems. In this work, we propose a new method for structural system identification (SI), uncertainty quantification, and validation directly from data. Inspired by generative modeling frameworks, a neural network maps random noise to physically meaningful parameters. These parameters are then used in the known equation of motion to obtain fake accelerations, which are compared to real training data via a mean square error loss. To simultaneously validate the learned parameters, we use independent validation datasets. The generated accelerations from these datasets are evaluated by a discriminator network, which determines whether the output is real or fake, and guides the parameter-generator network. Analytical and real experiments show the parameter estimation accuracy and model validation for different nonlinear structural systems.

Fox News chief political anchor Bret Baier has the latest on regulatory uncertainty amid AI development on'Special Report.' DON'T OVERSHARE DEETS: Have you ever stopped to think about how much your chatbot knows about you? Over the years, tools like ChatGPThave become incredibly adept at learning your preferences, habits and even some of your deepest secrets. But while this can make them seem more helpful and personalized, it also raises some serious privacy concerns. As much as you learn from these AI tools, they learn just as much about you.

This robot figures to revolutionize enterprise operations, particularly in the logistics and manufacturing sectors. In a groundbreaking development, California-based robotics and artificial intelligence (AI) company Dexterity has unveiled Mech, the world's first industrial super-humanoid robot. This innovative creation figures to revolutionize enterprise operations, particularly in the logistics and manufacturing sectors. Let's dive into the details of this new technology and explore its potential impact on the industry. GET SECURITY ALERTS & EXPERT TECH TIPS -- SIGN UP FOR KURT'S THE CYBERGUY REPORT NOW This industrial super-humanoid robot features two arms mounted on a rover, allowing it to navigate warehouses and industrial sites with ease.

The soundtrack to the spider-bot-crawling 1997 Ghost in the Shell game adaptation is coming to the West for the first time. Titled Ghost in the Shell: Megatech Body (as an ode to the Fuchikoma mech you pilot in the game), the soundtrack was produced by Takkyu Ishino. The PS1 game adaptation had late-90s gamers piloting a spider-like mech (first appearing in the 1991 manga), blasting enemies to smithereens with twin machine guns and guided missiles. Masamune Shirow, the original manga's author, wrote and illustrated its story and art design. But as 90s shooters often figured out, firing guns nonstop for hours on end is much better with a badass techno soundtrack pumping in the background like an energy drink for your ears. In addition to Ishino, it includes "warehouse-shaking bangers" from Mijk Van Dijk, The Advent, Joey Beltram and Brother from Another Planet (among others).

The intrinsic modularity and reconfigurability of modular reconfigurable robots (MRR) confer advantages such as versatility, fault tolerance, and economic efficacy, thereby showcasing considerable potential across diverse applications. The continuous evolution of the technology landscape and the emergence of diverse conceptual designs have generated multiple MRR categories, each described by its respective morphology or capability characteristics, leading to some ambiguity in the taxonomy. This paper conducts a comprehensive survey encompassing the entirety of MRR hardware and design, spanning from the inception in 1985 to 2023. This paper introduces an innovative, unified conceptual framework for understanding MRR hardware, which encompasses three pivotal elements: connectors, actuators, and homogeneity. Through the utilization of this trilateral framework, this paper provide an intuitive understanding of the diverse spectrum of MRR hardware iterations while systematically deciphering and classifying the entire range, offering a more structured perspective. This survey elucidates the fundamental attributes characterizing MRRs and their compositional aspects, providinig insights into their design, technology, functionality, and categorization. Augmented by the proposed trilateral framework, this paper also elaborates on the trajectory of evolution, prevailing trends, principal challenges, and potential prospects within the field of MRRs.

Before becoming a household name in gaming circles, he cut his teeth working on the studio's long-running Armored Core series, serving as a planner on 2005's Armored Core: Last Raven and then as director on Armored Core IV and Armored Core: For Answer. Following the success of Demon's Souls and Dark Souls, FromSoftware went on to release two more Armored Core games, though Miyazaki wasn't directly involved in those projects. Since then, the studio has been busy building on the Souls series, culminating with the runaway success of Elden Ring. Now, for the first time in nearly a decade, From is revisiting its mech franchise. Armored Core VI: Fires of Rubicon also marks the directorial debut of one of the studio's most promising up-and-coming talents -- Masaru Yamamura the lead game designer on Sekiro: Shadows Die Twice, and a designer on Bloodborne. Armored Core VI is not a Soulslike, but a lot of its best ideas feel informed by Sekiro and Bloodborne.

Numerical modeling of localizations is a challenging task due to the evolving rough solution in which the localization paths are not predefined. Despite decades of efforts, there is a need for innovative discretization-independent computational methods to predict the evolution of localizations. In this work, an improved version of the neural network-enhanced Reproducing Kernel Particle Method (NN-RKPM) is proposed for modeling brittle fracture. In the proposed method, a background reproducing kernel (RK) approximation defined on a coarse and uniform discretization is enriched by a neural network (NN) approximation under a Partition of Unity framework. In the NN approximation, the deep neural network automatically locates and inserts regularized discontinuities in the function space. The NN-based enrichment functions are then patched together with RK approximation functions using RK as a Partition of Unity patching function. The optimum NN parameters defining the location, orientation, and displacement distribution across location together with RK approximation coefficients are obtained via the energy-based loss function minimization. To regularize the NN-RK approximation, a constraint on the spatial gradient of the parametric coordinates is imposed in the loss function. Analysis of the convergence properties shows that the solution convergence of the proposed method is guaranteed. The effectiveness of the proposed method is demonstrated by a series of numerical examples involving damage propagation and branching.

Machine Learning (ML) has widely been used for modeling and predicting physical systems. These techniques offer high expressive power and good generalizability for interpolation within observed data sets. However, the disadvantage of black - box m odels is that they underperform under blind conditions since no physical knowledge is incorporated. Physics - based ML aims to address this problem by retaining the mathematical flexibility of ML techniques while incorporating physics. In accord, this paper proposes to embed mechanics - based models into the mean function of a Gaussian Process (GP) model and characterize potential discrepancies through kernel machines. A specific class of kernel function is promoted, which has a connection with the gradient of the physics - based model with respect to the input and parameters and shares similarity with the exact Auto - covariance function of linear dynamical systems. The spectral properties of the kernel function enable considering dominant periodic processes origin ating from physics misspecification. Nevertheless, the stationarity of the kernel function is a difficult hurdle in the sequential processing of long data sets, resolved through hierarchical Bayesian techniques. This implementation is also advantageous to mitigate computational costs, alleviating the scalability of GPs when dealing with sequential data. Using numerical and experimental examples, potential applications of the proposed method to structural dynamics inverse problems are demonstrated. Postdoctoral Fellow, Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Hong Kong, Email: osedehi@connect.ust.hk Ph.D. Student, Department of Civil and Environmental Engineering, The Hong Kong Universi ty of Science and Technology, Hong Kong, Email: akosikova@connect.ust.hk

William Zachary Reed loved building elaborate castles throughout Fortnite's grassy knolls. The zany mechanics that anchored the colorful battle royale were second-to-none, until giant robot mecha suits spoiled his fun whenever he would jump into a match. Those suits, aptly named BRUTE, gave two players all the firepower, health, and mobility needed to melt Reed's health bar in seconds. At that point in early 2019, it was clear that the battle royale's game mechanics had gotten a little too zany for him, and that was long before IP crossovers gave Dragon Ball Z's Goku a shotgun and taught Marvel's Moon Knight to floss. "I despised them," says Reed, who goes by the name KingYoshi online.

Around here we love Into the Breach, the 2018 tactical RPG-slash-chess game-slash-giant robot power fantasy from FTL developer Subset Games. The deceptively simple title hasn't lost any of its charm in the last few years, but today there's an expansion out that's adding a few thousand tons of content (get it, because the mechs are big?) to the sterling formula. Best of all, this "Advanced Edition" is a completely free update to both existing owners and new players. If you're new to Into the Breach, it's been described as "chess with giant robots." That's a bit reductive -- chess starts off with no less than 32 pieces, and Into the Breach rarely has even half that many on the board -- but then, so is the game itself.