Today's rechargeable batteries are a wonder, but far from perfect. Eventually, they all wear out, begetting expensive replacements and recycling. "But what if batteries were indestructible?" asks William Chueh, an associate professor of materials science and engineering at Stanford University and senior author of a new paper detailing a first-of-its-kind analytical approach to building better batteries that could help speed that day. The study appears in the journal Nature Materials. Chueh, lead author Haitao "Dean" Deng, PhD '21, and collaborators at Lawrence Berkeley National Laboratory, MIT and other research institutions used artificial intelligence to analyze new kinds of atomic-scale microscopic images to understand exactly why batteries wear out.

Data science covers the full spectrum of deriving insight from data, from initial data gathering and interpretation, via processing and engineering of data, and exploration and modeling, to eventually producing novel insights and decision support systems. Data science can be viewed as overlapping or broader in scope than other data-analytic methodological disciplines, such as statistics, machine learning, databases, or visualization.10 To illustrate the breadth of data science, consider, for example, the problem of recommending items (movies, books, or other products) to customers. While the core of these applications can consist of algorithmic techniques such as matrix factorization, a deployed system will involve a much wider range of technological and human considerations. These range from scalable back-end transaction systems that retrieve customer and product data in real time, experimental design for evaluating system changes, causal analysis for understanding the effect of interventions, to the human factors and psychology that underlie how customers react to visual information displays and make decisions. As another example, in areas such as astronomy, particle physics, and climate science, there is a rich tradition of building computational pipelines to support data-driven discovery and hypothesis testing. For instance, geoscientists use monthly global landcover maps based on satellite imagery at sub-kilometer resolutions to better understand how the Earth's surface is changing over time.50 These maps are interactive and browsable, and they are the result of a complex data-processing pipeline, in which terabytes to petabytes of raw sensor and image data are transformed into databases of a6utomatically detected and annotated objects and information. This type of pipeline involves many steps, in which human decisions and insight are critical, such as instrument calibration, removal of outliers, and classification of pixels. The breadth and complexity of these and many other data science scenarios means the modern data scientist requires broad knowledge and experience across a multitude of topics. Together with an increasing demand for data analysis skills, this has led to a shortage of trained data scientists with appropriate background and experience, and significant market competition for limited expertise. Considering this bottleneck, it is not surprising there is increasing interest in automating parts, if not all, of the data science process.

The success of machine learning for a wide range of applications has come with serious costs. The largest deep neural networks can have hundreds of billions of parameters that need to be tuned to mammoth datasets. This computationally intensive training process can cost millions of dollars, as well as large amounts of energy and associated carbon. Inference, the subsequent application of a trained model to new data, is less demanding for each use, but for widely used applications, the cumulative energy use can be even greater. "Typically there will be more energy spent on inference than there is on training," said David Patterson, Professor Emeritus at the University of California, Berkeley, and a Distinguished Engineer at Google, who in 2017 shared ACM's A.M. Turing Award.

In this webinar, participants will learn all about basic fundamentals of machine learning and their applications in Microgrids, as well as how to develop machine learning applications. Dr. Shashikant Madhukar Bakre completed his engineering education -Bachelor of Engineering from Nagpur University and Master of Engineering from Pune University. He completed management education, – Master of Management Studies from Pune University. He received his Ph.D. from Bharati Vidyapeeth, Pune in Electrical Engineering in the year 2011. He has a vast experience of teaching various subjects in Electrical Engineering, Information Technology and Management at the institutions namely Symbiosis Institute of Business Management (S.I.B.M.), Cusrow Wadia Institute of Technology (C.W.I.T.) and Institute for Studies in Technology and Management (I.S.T.M.).

The world needs more renewable energy to combat climate change, and for many countries hydropower is a key part of their renewable energy mix. But we also need to protect the continuity of river systems and the unique ecosystems they support. So how do we balance the two goals? TNC's Hydropower by Design work has been a cornerstone of our conservation efforts as we respond to this challenge. A new paper in Science details an effort to use artificial intelligence (AI) to find ways to mitigate the environmental impacts of hydropower across the Amazon--3 million miles of river and 2.4 million square miles of forest--while achieving energy production goals.

Technological discoveries are the spermatozoa of social change, says C.L.R James. That means future technology trends will change with respect to dynamic social trends. And these social trends changes with respect to changing needs and demands. There are several reasons behind rapidly changing tech trends such as coping with scarcity of resources, improving life standards, increasing overall efficiency and cutting extra costs and much more. These days, both small scale and big companies are inventing innovative tech things that seem to be magical stuff for many people.

Builders Show) and KBIS (Kitchen and Bath Industry Show) was a bevy of announcements--everything from 3D printing to software to appliances. Parks Associates found steady purchase intentions for smart appliances and continued growth for smart energy and lighting products at the show. But here is what stood out to me: Roughly 25% of U.S. broadband households plan to buy a smart appliance in the next six months. I was able to see firsthand how new appliances are becoming greener and more ecofriendly. They are better for the world and save energy, while also lengthening the life of our food.

This isn't about the time I inadvertently shut down one of Britain's nuclear power stations, an entirely true story for which the world is nevertheless not yet prepared. Nor is it about the poor junior developer who accidentally destroyed the production database on their first day (they'll be fine, bless them). Instead, I want to talk about another kind of career ending mistake, one that affects more than just the unlucky few. By "the end", I don't necessarily mean picking your retirement date. What we're really talking about is the aim or goal of your career.

In particular, PINNs do not require meshes and can efficiently solve forward problems and even ill-posed inverse problems, which are otherwise difficult or sometimes even impossible to solve using traditional numerical methods. Moreover, PINN can easily handle noisy, sparse and multi-fidelity data sets. The main advantage of the PINN methodology is that it can seamlessly incorporate all the given information like governing equation, experimental data, initial/boundary conditions into the loss function, thereby recasting the original problem into an equivalent optimization problem. Recently, Shin et al. [16] established the mathematical foundation of PINNs for linear partial differential equations, whereas in [17], Mishra and Molinaro presented estimate on the generalization error of the PINN methodology. In this work, we consider inverse problems of the shock wave problems in supersonic compressible flows. The governing equations of such flows are compressible Euler equations, which admit discontinuities or shocks, even though the initial states are smooth. Such ill-posed inverse problems are difficult or even sometimes impossible to solve using the traditional numerical solvers. Moreover, for the shock wave problem, the traditional numerical methods usually require boundary conditions (BCs) for all field variables.

Planet induced sub-structures, like annular gaps, observed in dust emission from protoplanetary disks provide a unique probe to characterize unseen young planets. While deep learning based model has an edge in characterizing the planet's properties over traditional methods, like customized simulations and empirical relations, it lacks in its ability to quantify the uncertainty associated with its predictions. In this paper, we introduce a Bayesian deep learning network "DPNNet-Bayesian" that can predict planet mass from disk gaps and provides uncertainties associated with the prediction. A unique feature of our approach is that it can distinguish between the uncertainty associated with the deep learning architecture and uncertainty inherent in the input data due to measurement noise. The model is trained on a data set generated from disk-planet simulations using the \textsc{fargo3d} hydrodynamics code with a newly implemented fixed grain size module and improved initial conditions. The Bayesian framework enables estimating a gauge/confidence interval over the validity of the prediction when applied to unknown observations. As a proof-of-concept, we apply DPNNet-Bayesian to dust gaps observed in HL Tau. The network predicts masses of $ 86.0 \pm 5.5 M_{\Earth} $, $ 43.8 \pm 3.3 M_{\Earth} $, and $ 92.2 \pm 5.1 M_{\Earth} $ respectively, which are comparable to other studies based on specialized simulations.