Timely completion of design cycles for multiscale and multiphysics systems ranging from consumer electronics to hypersonic vehicles relies on rapid simulation-based prototyping. The latter typically involves high-dimensional spaces of possibly correlated control variables (CVs) and quantities of interest (QoIs) with non-Gaussian and/or multimodal distributions. We develop a model-agnostic, moment-independent global sensitivity analysis (GSA) that relies on differential mutual information to rank the effects of CVs on QoIs. Large amounts of data, which are necessary to rank CVs with confidence, are cheaply generated by a deep neural network (DNN) surrogate model of the underlying process. The DNN predictions are made explainable by the GSA so that the DNN can be deployed to close design loops. Our information-theoretic framework is compatible with a wide variety of black-box models. Its application to multiscale supercapacitor design demonstrates that the CV rankings facilitated by a domain-aware Graph-Informed Neural Network are better resolved than their counterparts obtained with a physics-based model for a fixed computational budget. Consequently, our information-theoretic GSA provides an "outer loop" for accelerated product design by identifying the most and least sensitive input directions and performing subsequent optimization over appropriately reduced parameter subspaces.

Multivariate time series forecasting is widely used in various fields. Reasonable prediction results can assist people in planning and decision-making, generate benefits and avoid risks. Normally, there are two characteristics of time series, that is, long-term trend and short-term fluctuation. For example, stock prices will have a long-term upward trend with the market, but there may be a small decline in the short term. These two characteristics are often relatively independent of each other. However, the existing prediction methods often do not distinguish between them, which reduces the accuracy of the prediction model. In this paper, a MTS forecasting framework that can capture the long-term trends and short-term fluctuations of time series in parallel is proposed. This method uses the original time series and its first difference to characterize long-term trends and short-term fluctuations. Three prediction sub-networks are constructed to predict long-term trends, short-term fluctuations and the final value to be predicted. In the overall optimization goal, the idea of multi-task learning is used for reference, which is to make the prediction results of long-term trends and short-term fluctuations as close to the real values as possible while requiring to approximate the values to be predicted. In this way, the proposed method uses more supervision information and can more accurately capture the changing trend of the time series, thereby improving the forecasting performance.

Lawrence Livermore National Laboratory (LLNL) and AI company Cerebras Systems today announced the integration of the 1.2-trillion Cerebras' Wafer Scale Engine (WSE) chip into the National Nuclear Security Administration's (NNSA) 23-petaflop Lassen supercomputer. The pairing of Lassen's simulation capability with Cerebras' machine learning compute system, along with the CS-1 accelerator system that houses the chip, makes LLNL "the first institution to integrate the AI platform with a large-scale supercomputer and creates a radically new type of computing solution, enabling researchers to investigate novel approaches to predictive modeling," according to the lab. Work on initial AI models began last month. Lassen is the "unclassified companion," according to LLNL, to the IBM/Nvidia system Sierra (ranked no. 3 on the Top500 list of the world's most powerful supercomputer) and is no. Funded by the NNSA's Advanced Simulation and Computing program, the platform aims to accelerate solutions for Department of Energy and NNSA national security mission applications.

Energy investors in search of resilience are rediscovering the power of IoT and machine learning to guide data-driven decisions in the face of an increasingly volatile economic environment. Much has happened since I first wrote about a cloud-based IoT platform from Kaiserwetter Energy Asset Management called ARISTOTELES. ARISTOTELES is at the forefront of informed decision-making platforms supporting all stakeholders involved in renewable energy investments. "We're seeing strong interest from investors who want to stay ahead of ongoing shocks related to the COVID-19 pandemic, as well as all the other variables that impact the energy industry," said Hanno Schoklitsch, CEO and founder at Kaiserwetter Energy Asset Management. "Our customers have been super-astonished when they've seen how they could use applied data intelligence on a daily basis to benchmark the performance of the assets they have invested in, and predict production outcomes of renewable energy facilities throughout the world at any given time."

Owing to the global lockdowns that resulted from the COVID-19 pandemic, fuel demand plummeted and the price of oil futures went negative in April 2020. Robust fuel demand projections are crucial to economic and energy planning and policy discussions. Here we incorporate pandemic projections and people’s resulting travel and trip activities and fuel usage in a machine-learning-based model to project the US medium-term gasoline demand and study the impact of government intervention. We found that under the reference infection scenario, the US gasoline demand grows slowly after a quick rebound in May, and is unlikely to fully recover prior to October 2020. Under the reference and pessimistic scenario, continual lockdown (no reopening) could worsen the motor gasoline demand temporarily, but it helps the demand recover to a normal level quicker. Under the optimistic infection scenario, gasoline demand will recover close to the non-pandemic level by October 2020. The COVID 19 pandemic and consequent lockdown has had a substantial impact on mobility and therefore fuel demand and it is not clear when demand will recover. Ou et al. use a machine learning model that integrates health recovery scenarios to project the near-term future of gasoline demand.

With the advancement of science and technology, the number of academic papers published in the world each year has increased almost exponentially. While a large number of research papers highlight the prosperity of science and technology, they also give rise to some problems. As we all know, academic papers are the most intuitive embodiment of the research results of scholars, which can reflect the level of researchers. It is also the evaluation standard for decision-making such as promotion and allocation of funds. Therefore, how to measure the quality of an academic paper is very important. The most common standard for measuring academic papers is the number of citation counts of papers, because this indicator is widely used in the evaluation of scientific publications, and it also serves as the basis for many other indicators (such as the h-index). Therefore, it is very important to be able to accurately predict the citation counts of academic papers. This paper proposes an end-to-end deep learning network, DeepCCP, which combines the effect of information cascade and looks at the citation counts prediction problem from the perspective of information cascade prediction. DeepCCP directly uses the citation network formed in the early stage of the paper as the input, and the output is the citation counts of the corresponding paper after a period of time. DeepCCP only uses the structure and temporal information of the citation network, and does not require other additional information, but it can still achieve outstanding performance. According to experiments on 6 real data sets, DeepCCP is superior to the state-of-the-art methods in terms of the accuracy of citation count prediction.

Recent advancements in quantum computing have driven the scientific community's quest to solve a certain class of complex problems for which quantum computers would be better suited than traditional supercomputers. To improve the efficiency with which quantum computers can solve these problems, scientists are investigating the use of artificial intelligence approaches. In a new study, scientists at the U.S. Department of Energy's (DOE) Argonne National Laboratory have developed a new algorithm based on reinforcement learning to find the optimal parameters for the Quantum Approximate Optimization Algorithm (QAOA), which allows a quantum computer to solve certain combinatorial problems such as those that arise in materials design, chemistry and wireless communications. "It's a bit like having a self-driving car in traffic; the algorithm can detect when it needs to make adjustments in the'dials' it uses to do the computation." "Combinatorial optimization problems are those for which the solution space gets exponentially larger as you expand the number of decision variables," said Argonne computer scientist Prasanna Balaprakash.

Successful teaching requires an assumption of how the learner learns - how the learner uses experiences from the world to update their internal states. We investigate what expectations people have about a learner when they teach them in an online manner using rewards and punishment. We focus on a common reinforcement learning method, Q-learning, and examine what assumptions people have using a behavioral experiment. To do so, we first establish a normative standard, by formulating the problem as a machine teaching optimization problem. To solve the machine teaching optimization problem, we use a deep learning approximation method which simulates learners in the environment and learns to predict how feedback affects the learner's internal states. What do people assume about a learner's learning and discount rates when they teach them an idealized exploration-exploitation task? In a behavioral experiment, we find that people can teach the task to Q-learners in a relatively efficient and effective manner when the learner uses a small value for its discounting rate and a large value for its learning rate. However, they still are suboptimal. We also find that providing people with real-time updates of how possible feedback would affect the Q-learner's internal states weakly helps them teach. Our results reveal how people teach using evaluative feedback and provide guidance for how engineers should design machine agents in a manner that is intuitive for people.

California is becoming a poster child for the risks utilities face from climate change, from power lines starting wildfires to heat waves forcing increasingly renewable-powered grids to the brink of system collapse. But utilities around the world are facing similar risks as they seek to decarbonize their generation fleets and make their grids more resilient to extreme weather events that are becoming more extreme and more common. While the costs of mitigating those risks are hard to quantify, they're likely much smaller than the costs of doing nothing and facing the alternatives. We're seeing this calculation reflected in many ways, from massive asset manager BlackRock's decision to move away from investments in coal and other global-warming-causing industries, to the maintenance and planning failures that led to the power-line-sparked wildfires that forced Pacific Gas & Electric into bankruptcy last year. Data -- the lifeblood of investors, insurers and other professional calculators of risk -- can help utilities better identify these climate-change challenges and optimize their methods to mitigate them.

Since Ring launched its first video doorbell in 2014, the popular Amazon-owned smart home brand has dominated the smart doorbell market, churning out three new doorbells this year alone. That said, with so many smart video doorbells at your disposal, you might be scratching your head trying to make a decision. To help you in your quest, we're taking a closer look at the differences between two of Ring's most popular new releases: the Ring Video Doorbell (second-gen), an upgrade to Ring's original video doorbell, and the new Ring Video Doorbell 3 Plus. Here's how these two popular doorbells stack up. The Ring Video Doorbell (second-gen) is more affordable than the Ring Video Doorbell 3 Plus. Typically, video doorbells cost between $100 and $200, and Ring's most recent doorbell cameras pretty much hit this sweet spot.