Full Wave Inversion (FWI) imaging scheme has many applications in engineering, geoscience and medical sciences. In this paper, a surrogate deep learning FWI approach is presented to quantify properties of materials using stress waves. Such inverse problems, in general, are ill-posed and nonconvex, especially in cases where the solutions exhibit shocks, heterogeneity, discontinuities, or large gradients. The proposed approach is proven efficient to obtain global minima responses in these cases. This approach is trained based on random sampled set of material properties and sampled trials around local minima, therefore, it requires a forward simulation can handle high heterogeneity, discontinuities and large gradients. High resolution Kurganov-Tadmor (KT) central finite volume method is used as forward wave propagation operator. Using the proposed framework, material properties of 2D media are quantified for several different situations. The results demonstrate the feasibility of the proposed method for estimating mechanical properties of materials with high accuracy using deep learning approaches.

Applying machine learning models to meteorological data brings many opportunities to the Geosciences field, such as predicting future weather conditions more accurately. In recent years, modeling meteorological data with deep neural networks has become a relevant area of investigation. These works apply either recurrent neural networks (RNNs) or some hybrid approach mixing RNNs and convolutional neural networks (CNNs). In this work, we propose STConvS2S (short for Spatiotemporal Convolutional Sequence to Sequence Network), a new deep learning architecture built for learning both spatial and temporal data dependencies in weather data, using fully convolutional layers. Computational experiments using observations of air temperature and rainfall show that our architecture captures spatiotemporal context and outperforms baseline models and the state-of-art architecture for weather forecasting task.

High-resolution radar and night vision cameras may help scientists protect bats from untimely deaths at wind farms, according to new research. Researchers are using these technologies to provide more specific details about the number of bats killed by wind turbines in Iowa. These details will improve scientists' understanding of bat activity and potentially save their lives, said Jian Teng, a graduate researcher at the University of Iowa who presented the work this week at the 2019 American Geophysical Union Fall Meeting in San Francisco. This work has broad impacts, according to Teng. "The more bats you kill, the more insects you have on farms; then, farmers will put more pesticides; and then, people will eat more pesticides," he said.

WIRE)--Citrine Informatics has been named an Enterprise of the Year Gold winner in the Best in Biz Awards, the only independent business awards program judged by prominent editors and reporters from top-tier publications in North America. Citrine Informatics' artificial intelligence technology is used by the world's largest materials and chemicals companies to accelerate the product development cycle. Since 2011, Best in Biz Awards' entrants have spanned the spectrum, from the most innovative local companies and start-ups to some of the most recognizable global brands. With more than 700 entries, the 9th annual program attracted a record number of entries from an impressive array of public and private companies of all sizes and spanning all geographic regions and industries in the U.S. and Canada. Best in Biz Awards 2019 honors were conferred in 80 different categories, including Company of the Year, Fastest-Growing Company, Most Innovative Company, Best Place to Work, Customer Service Department, Executive of the Year, Most Innovative Product, Enterprise Product, Best New Service, CSR Program, Event and Blog of the Year.

Artificial Intelligence is on everyone's lips right now. It is the fastest growing branch of the high-tech industry. The German government sees AI as a key strategy for mastering some of the greatest challenges of our time, such as climate change and pollution. It is difficult to establish a clear differentiation of Artificial Intelligence or even a precise definition. AI is often used in connection or sometimes even synonymous with the terms machine learning, big data, or deep learning.

Progress in Machine Learning (ML) in recent years has been meteorical, with major breakthroughs happening in domains such as machine translation, image recognition and generation. While these advances are having widespread applications in many domains, detecting cancers on X-rays and improving the prediction of supply and demand,the carbon impact of ML training has not been a central part of the conversation until recently. Therefore, as ML practitioners who are also aware of the overall state of the environment, we find that it is important to develop this conversion further and work towards building the tools we need to assess the carbon emissions generated by the models we train, as well as ways to reduce those emissions. We have published this short paper to the Climate Change AI workshop at NeurIPS 2019. The sources are available in the csv file above.

With the help of artificial intelligence, BP says it needs 40% fewer workers to keep its natural gas ... [ ] flowing in Wyoming. A visitor to one of BP's natural gas fields in Wyoming a few years ago might have noticed an odd sight: smartphones in plastic bags tied to pumps with zip ties. This was an early test of a multistate initiative by the oil giant to link a network of Wi-Fi sensors to an artificial intelligence system--one that now operates the Wamsutter field in Wyoming with far less human oversight than before. Artificial intelligence has come to the oil patch, accelerating a technical change that is transforming the conditions for the oil and gas industry's 150,000 U.S. workers. Giant energy companies like Shell and BP are investing billions to bring artificial intelligence to new refineries, oilfields and deepwater drilling platforms.

BOSTON--In July 2018, Curtis Berlinguette, a materials scientist at the University of British Columbia in Vancouver, Canada, realized he was wasting his graduate student's time and talent. He had asked her to refine a key material in solar cells to boost its electrical conductivity. But the number of potential tweaks was overwhelming, from spiking the recipe with traces of metals and other additives to varying the heating and drying times. "There are so many things you can go change, you can quickly go through 10 million [designs] you can test," Berlinguette says. So he and colleagues outsourced the effort to a single-armed robot overseen by an artificial intelligence (AI) algorithm.

At the IEEE International Electron Devices Meeting (IEDM) in San Francisco this week, France-based research institute CEA-Leti presented papers highlighting its achievements in bio-inspired neural networks, a readout technique for high-fidelity measurements in large quantum dot arrays and inorganic thin film batteries with optimum energy and power density performance for medical and implantable devices. This article presents highlights of each of these three papers. Bio-inspired neural networks have been in development for a while, and at IEDM, Leti announced it had fabricated a fully integrated bio-inspired neural network, combining resistive-RAM-based synapses and analog spiking neurons. The functionality of this proof-of-concept circuit was demonstrated thanks to handwritten digits classification. "The entire network is integrated on-chip," said Alexandre Valentian, lead author of the paper, Fully Integrated Spiking Neural Network with Analog Neurons and RRAM Synapses.

The computational cost associated with simulating fluid flows can make it infeasible to run many simulations across multiple flow conditions. Building upon concepts from generative modeling, we introduce a new method for learning neural network models capable of performing efficient parameterized simulations of fluid flows. Evaluated on their ability to simulate both two-dimensional and three-dimensional fluid flows, trained models are shown to capture local and global properties of the flow fields at a wide array of flow conditions. Furthermore, flow simulations generated by the trained models are shown to be orders of magnitude faster than the corresponding computational fluid dynamics simulations.