Constitutive modeling based on continuum mechanics theory has been a classical approach for modeling the mechanical responses of materials. However, when constitutive laws are unknown or when defects and/or high degrees of heterogeneity are present, these classical models may become inaccurate. In this work, we propose to use data-driven modeling, which directly utilizes high-fidelity simulation and/or experimental measurements to predict a material's response without using conventional constitutive models. Specifically, the material response is modeled by learning the implicit mappings between loading conditions and the resultant displacement and/or damage fields, with the neural network serving as a surrogate for a solution operator. To model the complex responses due to material heterogeneity and defects, we develop a novel deep neural operator architecture, which we coin as the Implicit Fourier Neural Operator (IFNO). In the IFNO, the increment between layers is modeled as an integral operator to capture the long-range dependencies in the feature space. As the network gets deeper, the limit of IFNO becomes a fixed point equation that yields an implicit neural operator and naturally mimics the displacement/damage fields solving procedure in material modeling problems. We demonstrate the performance of our proposed method for a number of examples, including hyperelastic, anisotropic and brittle materials. As an application, we further employ the proposed approach to learn the material models directly from digital image correlation (DIC) tracking measurements, and show that the learned solution operators substantially outperform the conventional constitutive models in predicting displacement fields.

Metal-organic frameworks, a class of materials with porous molecular structures, have a variety of possible applications, such as capturing harmful gases and catalyzing chemical reactions. Made of metal atoms linked by organic molecules, they can be configured in hundreds of thousands of different ways. To help researchers sift through all of the possible metal-organic framework (MOF) structures and help identify the ones that would be most practical for a particular application, a team of MIT computational chemists has developed a model that can analyze the features of a MOF structure and predict if it will be stable enough to be useful. The researchers hope that these computational predictions will help cut the development time of new MOFs. "This will allow researchers to test the promise of specific materials before they go through the trouble of synthesizing them," says Heather Kulik, an associate professor of chemical engineering at MIT.

In its early days, robotic process automation emerged from rudimentary screen scraping, macros and workflow automation software. Once a script-heavy and limited tool that was almost exclusively used to perform mundane tasks for individual users, RPA has evolved into an enterprisewide megatrend that puts automation at the center of digital business initiatives. In this Breaking Analysis, we present our quarterly update of the trends in RPA and share the latest survey data from Enterprise Technology Research. The new momentum in RPA is around enterprisewide automation initiatives. Once exclusively focused on back office automation in areas such as finance, RPA has now become an enterprise transformation catalyst for many larger organizations.

Metallic-organic frameworks, a category of supplies with porous molecular buildings, have quite a lot of attainable purposes, similar to capturing dangerous gases and catalyzing chemical reactions. Product of steel atoms linked by natural molecules, they are often configured in tons of of hundreds of various methods. To assist researchers sift by means of all the attainable metal-organic framework (MOF) buildings and assist determine those that might be most sensible for a specific software, a staff of MIT computational chemists has developed a mannequin that may analyze the options of a MOF construction and predict if it will likely be secure sufficient to be helpful. The researchers hope that these computational predictions will assist lower the event time of recent MOFs. "This can enable researchers to check the promise of particular supplies earlier than they undergo the difficulty of synthesizing them," says Heather Kulik, an affiliate professor of chemical engineering at MIT.

Greg Kruger pauses for what seems like an eternity during his presentation, but it actually just lasts six seconds. The senior agronomist for BASF's xarvio digital farming division did it to prove a point about BASF's Smart Farming joint collaboration with Bosch that includes precision spraying technology the firms call Smart Spraying. The strategy teams machine-learning algorithms with computer vision to enable "green-on-green" spraying that distinguishes between weeds and crops in-season. Kruger's presentation was part of a BASF media briefing held before this week's Commodity Classic in New Orleans. "In the six seconds that I paused, we've taken 1,000 images [with Smart Spraying] on the boom," says Kruger.

We address a physics-informed neural network based on the concept of random projections for the numerical solution of IVPs of nonlinear ODEs in linear-implicit form and index-1 DAEs, which may also arise from the spatial discretization of PDEs. The scheme has a single hidden layer with appropriately randomly parametrized Gaussian kernels and a linear output layer, while the internal weights are fixed to ones. The unknown weights between the hidden and output layer are computed by Newton's iterations, using the Moore-Penrose pseudoinverse for low to medium, and sparse QR decomposition with regularization for medium to large scale systems. To deal with stiffness and sharp gradients, we propose a variable step size scheme for adjusting the interval of integration and address a continuation method for providing good initial guesses for the Newton iterations. Based on previous works on random projections, we prove the approximation capability of the scheme for ODEs in the canonical form and index-1 DAEs in the semiexplicit form. The optimal bounds of the uniform distribution are parsimoniously chosen based on the bias-variance trade-off. The performance of the scheme is assessed through seven benchmark problems: four index-1 DAEs, the Robertson model, a model of five DAEs describing the motion of a bead, a model of six DAEs describing a power discharge control problem, the chemical Akzo Nobel problem and three stiff problems, the Belousov-Zhabotinsky, the Allen-Cahn PDE and the Kuramoto-Sivashinsky PDE. The efficiency of the scheme is compared with three solvers ode23t, ode23s, ode15s of the MATLAB ODE suite. Our results show that the proposed scheme outperforms the stiff solvers in several cases, especially in regimes where high stiffness or sharp gradients arise in terms of numerical accuracy, while the computational costs are for any practical purposes comparable.

Research and innovation in artificial intelligence (AI) in the mining industry operations and technologies sector has declined in the last year.

Prototype drones capable of navigating dangerous and unpredictable environments without crashing could prove useful for search-and-rescue teams. Paolo De Petris at the Norwegian University of Science and Technology and his colleagues have developed a flying drone that aims to avoid crashes altogether. The robot, called RMF-Owl, made its debut while winning a competition hosted by the US Defense Advanced Research Projects Agency, in which it had to navigate an underground mine and perform rescue-related tasks.

BAN MAI, Thailand – As a child, Manit Boonkhiew watched his grandparents plow their rice farm near Bangkok with water buffaloes, and harvest by hand. His parents switched to tractors and threshers, while he now uses a zippy drone to spray pesticide on his field. Manit, who grows rice, orchids and fruit trees on about 40 acres (16 hectares) of land in Ban Mai, is part of a community enterprise that recently acquired a drone under a Thai government program to digitize agriculture. Drones to plant seeds, and spray pesticide and fertilizers are growing in popularity in the Southeast Asian country as it grapples with a labor shortage that worsened during the coronavirus pandemic, with restrictions on movement of workers. "Labor is the biggest challenge for us -- it's hard to get, and it's expensive," said Manit, 56, a leader of the Ban Mai Community Rice Center farm that comprises 57 members with nearly 400 acres of land.

AMP Robotics has extended its partnership with Evergreen, a producer of food-grade recycled polyethylene terephthalate (rPET). Evergreen now has 15 of AMP's robotic sorting systems installed or planned across three facilities. In addition to six robots in Clyde, Evergreen has added six in Riverside, California, and will soon add three in Albany, New York. AMP's technology identifies and sorts green and clear PET from post-consumer bales of plastic soft drink bottles at speeds up to three times faster and at a higher accuracy than manual sorters can achieve. Evergreen then recycles the material into reusable flakes or pellets, which it sells to end markets as feedstock for new containers and packaging.