Climate change mitigation is about reducing greenhouse gas (GHG) emissions. The worldwide goal is to reach net zero, which means balancing the amount of GHG emissions produced and the amount removed from the atmosphere. On the one hand, this implies reducing emissions by using low-carbon technologies and energy efficiency. On the other hand, it implies deploying negative emission technologies such as carbon storage, which is the subject of this post. Carbon capture and storage (CCS) refers to a group of technologies that contribute to directly reducing emissions at their source in key power sectors such as coal and gas power plants and industrial plants.

Real-world AI has revolutionized and changed how people live during the past decade, including media and entertainment, healthcare and life science, retail, automotive, finance service, manufacturing, and oil and gas. Speaking to a smart home device, browsing social media with recommended content, or taking a ride with a self-driving vehicle is no longer in the future. With the ease of your smartphone, you can now deposit checks without going to the bank? All of these advances have been made possible through new AI breakthroughs in software and hardware. At Microsoft, we host our deep learning inferencing, cognitive science, and our applied AI services on the NC series instances.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. As technology evolves to support a wide range of tasks, companies are increasingly relying on automation to help improve overall work efficiency and operations. Satellite analytics, specifically, is rapidly growing in popularity and helping businesses in a variety of industries including utilities, energy, mining, transportation, construction and more. In fact, SnapLogic released a report stating 81% of employees say AI improves their job performance. Satellites can travel around the earth at 17,000 mph, capturing hi-res images to provide companies access to historical data, increase safety and cost-efficient insights.

Photoacoustic tomography (PAT) is a hybrid biomedical imaging modality based on the photoacoustic effect [6, 44, 32]. In PAT, the imaged target is illuminated with a short pulse of light. Absorption of light creates localized areas of thermal expansion, resulting in localized pressure increases within the imaged target. This pressure distribution, called the initial pressure, relaxes as broadband ultrasound waves that are measured on the boundary of the imaged target. In the inverse problem of PAT, the initial pressure distribution is estimated from a set of measured ultrasound data.

This Director of AI & ML will be responsible for developing new models and systems to support Key Capture Energy's (KCE) battery storage facilities, as well as work closely with our software development and market operations analytics team to deploy models to production systems and utilize large-scale datasets for model development and optimization. Prior roles should include significant hands-on experience with typical AI/ML tasks such as feature engineering, feature selection, and hyperparameter tuning.

Eco Entrepreneur and Climate Campaigner, Dale Vince, OBE There's a saying that goes, 'don't meet your heroes as they may disappoint', well I pleased to say this wasn't the case when I spoke to green entrepreneur, Dale Vince, OBE for this episode for Irish Tech News Dale is one a what I would call a conscious CEO, where his work focuses on three key areas – energy, transport, and food. In 1995 he launched Ecotricity, the world's first green energy company, which today, powers around 200,000 homes and businesses across the UK with renewable energy from the wind and sun. Dale also owns Devil's Kitchen, which makes vegan school dinners, and his latest business, Skydiamond creates lab grown diamonds from the wind, rain and sun. If that wasn't enough, Dale is Chairman and owner of Forest Green Rovers recognised by FIFA as the "world's greenest football club" and became a United Nations Climate Champion in 2018. He launched his first book, Manifesto in 2020, and is Executive Producer of the Netflix Original documentary, Seaspiracy.

This article presents an in-depth review of the topic of path following for autonomous robotic vehicles, with a specific focus on vehicle motion in two dimensional space (2D). From a control system standpoint, path following can be formulated as the problem of stabilizing a path following error system that describes the dynamics of position and possibly orientation errors of a vehicle with respect to a path, with the errors defined in an appropriate reference frame. In spite of the large variety of path following methods described in the literature we show that, in principle, most of them can be categorized in two groups: stabilization of the path following error system expressed either in the vehicle's body frame or in a frame attached to a "reference point" moving along the path, such as a Frenet-Serret (F-S) frame or a Parallel Transport (P-T) frame. With this observation, we provide a unified formulation that is simple but general enough to cover many methods available in the literature. We then discuss the advantages and disadvantages of each method, comparing them from the design and implementation standpoint. We further show experimental results of the path following methods obtained from field trials testing with under-actuated and fully-actuated autonomous marine vehicles. In addition, we introduce open-source Matlab and Gazebo/ROS simulation toolboxes that are helpful in testing path following methods prior to their integration in the combined guidance, navigation, and control systems of autonomous vehicles.

A long standing problem in the modeling of non-Newtonian hydrodynamics of polymeric flows is the availability of reliable and interpretable hydrodynamic models that faithfully encode the underlying micro-scale polymer dynamics. The main complication arises from the long polymer relaxation time, the complex molecular structure and heterogeneous interaction. DeePN$^2$, a deep learning-based non-Newtonian hydrodynamic model, has been proposed and has shown some success in systematically passing the micro-scale structural mechanics information to the macro-scale hydrodynamics for suspensions with simple polymer conformation and bond potential. The model retains a multi-scaled nature by mapping the polymer configurations into a set of symmetry-preserving macro-scale features. The extended constitutive laws for these macro-scale features can be directly learned from the kinetics of their micro-scale counterparts. In this paper, we develop DeePN$^2$ using more complex micro-structural models. We show that DeePN$^2$ can faithfully capture the broadly overlooked viscoelastic differences arising from the specific molecular structural mechanics without human intervention.

Deep neural operators can learn operators mapping between infinite-dimensional function spaces via deep neural networks and have become an emerging paradigm of scientific machine learning. However, training neural operators usually requires a large amount of high-fidelity data, which is often difficult to obtain in real engineering problems. Here, we address this challenge by using multifidelity learning, i.e., learning from multifidelity datasets. We develop a multifidelity neural operator based on a deep operator network (DeepONet). A multifidelity DeepONet includes two standard DeepONets coupled by residual learning and input augmentation. Multifidelity DeepONet significantly reduces the required amount of high-fidelity data and achieves one order of magnitude smaller error when using the same amount of high-fidelity data. We apply a multifidelity DeepONet to learn the phonon Boltzmann transport equation (BTE), a framework to compute nanoscale heat transport. By combining a trained multifidelity DeepONet with genetic algorithm or topology optimization, we demonstrate a fast solver for the inverse design of BTE problems.

Solar power is the largest source of new energy in the world. Raptor Maps is a fast-growing, venture-backed, MIT-born climate tech startup that is building software to enable the solar energy industry to scale. Parties across the entire solar lifecycle use Raptor Maps' data model to manage ever growing utility-scale solar portfolios. We are an industry leader with hundreds of customers, including owners, builders, operators, and aerial service providers, across over 40 countries with 200 million solar panels under management. Our software platform is essential in the fight against climate change.