CHENNAI: The Indian Institute of Technology, Madras (IIT-M) on Monday said its researchers have developed algorithms that enable novel applications for artificial intelligence (AI), machine learning and deep learning to solve engineering problems. The researchers are going to establish a start-up to deploy their AI Software called'AISoft' to develop solutions to engineering problems in varied fields such as in thermal management, semiconductors, automobile, aerospace and electronic cooling applications. "We tested AIsoft and used it to solve such thermal management problems. We found it to be nearly million-fold faster compared to existing solutions currently used in the field," said Vishal Nandigana, Assistant Professor, Fluid Systems Laboratory, Department of Mechanical Engineering. "Our AI works on any generalised rectilinear and curvilinear input geometry.

Employees have a full spectrum of content and data, but it's easy to get lost in unproductive, dead-end hunts. Join this VB Live event to learn how AI-powered smart search boosts efficient data discovery and insights that deliver real-world, high-value solutions for complex problems of every size. Digital transformation means pivoting to become more efficient, data-driven, and nimble. Traditional enterprise search is anything but. To do their jobs, enterprise employees need to tap into a huge amount of content and data available both inside and outside the company, and the tools they're handed aren't up to the job, says Simon Taylor, vice president worldwide channels & alliances at Lucidworks.

Recent work has shown how to embed differentiable optimization problems (that is, problems whose solutions can be backpropagated through) as layers within deep learning architectures. This method provides a useful inductive bias for certain problems, but existing software for differentiable optimization layers is rigid and difficult to apply to new settings. In this paper, we propose an approach to differentiating through disciplined convex programs, a subclass of convex optimization problems used by domain-specific languages (DSLs) for convex optimization. We introduce disciplined parametrized programming, a subset of disciplined convex programming, and we show that every disciplined parametrized program can be represented as the composition of an affine map from parameters to problem data, a solver, and an affine map from the solver's solution to a solution of the original problem (a new form we refer to as affine-solver-affine form). We then demonstrate how to efficiently differentiate through each of these components, allowing for end-to-end analytical differentiation through the entire convex program. We implement our methodology in version 1.1 of CVXPY, a popular Python-embedded DSL for convex optimization, and additionally implement differentiable layers for disciplined convex programs in PyTorch and TensorFlow 2.0. Our implementation significantly lowers the barrier to using convex optimization problems in differentiable programs. We present applications in linear machine learning models and in stochastic control, and we show that our layer is competitive (in execution time) compared to specialized differentiable solvers from past work.

This paper tackles the challenge presented by small-data to the task of Bayesian inference. A novel methodology, based on manifold learning and manifold sampling, is proposed for solving this computational statistics problem under the following assumptions: 1) neither the prior model nor the likelihood function are Gaussian and neither can be approximated by a Gaussian measure; 2) the number of functional input (system parameters) and functional output (quantity of interest) can be large; 3) the number of available realizations of the prior model is small, leading to the small-data challenge typically associated with expensive numerical simulations; the number of experimental realizations is also small; 4) the number of the posterior realizations required for decision is much larger than the available initial dataset. The method and its mathematical aspects are detailed. Three applications are presented for validation: The first two involve mathematical constructions aimed to develop intuition around the method and to explore its performance. The third example aims to demonstrate the operational value of the method using a more complex application related to the statistical inverse identification of the non-Gaussian matrix-valued random elasticity field of a damaged biological tissue (osteoporosis in a cortical bone) using ultrasonic waves.

In the geophysical field, seismic noise attenuation has been considered as a critical and long-standing problem, especially for the pre-stack data processing. Here, we propose a model to leverage the deep-learning model for this task. Rather than directly applying an existing de-noising model from ordinary images to the seismic data, we have designed a particular deep-learning model, based on residual neural networks. It is named as N2N-Seismic, which has a strong ability to recover the seismic signals back to intact condition with the preservation of primary signals. The proposed model, achieving with great success in attenuating noise, has been tested on two different seismic datasets. Several metrics show that our method outperforms conventional approaches in terms of Signal-to-Noise-Ratio, Mean-Squared-Error, Phase Spectrum, etc. Moreover, robust tests in terms of effectively removing random noise from any dataset with strong and weak noises have been extensively scrutinized in making sure that the proposed model is able to maintain a good level of adaptation while dealing with large variations of noise characteristics and intensities.

This paper considers the problem of efficient exploration of unseen environments, a key challenge in AI. We propose a `learning to explore' framework where we learn a policy from a distribution of environments. At test time, presented with an unseen environment from the same distribution, the policy aims to generalize the exploration strategy to visit the maximum number of unique states in a limited number of steps. We particularly focus on environments with graph-structured state-spaces that are encountered in many important real-world applications like software testing and map building. We formulate this task as a reinforcement learning problem where the `exploration' agent is rewarded for transitioning to previously unseen environment states and employ a graph-structured memory to encode the agent's past trajectory. Experimental results demonstrate that our approach is extremely effective for exploration of spatial maps; and when applied on the challenging problems of coverage-guided software-testing of domain-specific programs and real-world mobile applications, it outperforms methods that have been hand-engineered by human experts.

--Uncertainty quantification for forward and inverse problems is a central challenge across physical and biomedical disciplines. We address this challenge for the problem of modeling subsurface flow at the Hanford Site by combining stochastic computational models with observational data using physics-informed GAN models. The geographic extent, spatial heterogeneity, and multiple correlation length scales of the Hanford Site require training a computationally intensive GAN model to thousands of dimensions. We develop a hierarchical scheme for exploiting domain parallelism, map discriminators and generators to multiple GPUs, and employ efficient communication schemes to ensure training stability and convergence. We developed a highly optimized implementation of this scheme that scales to 27,500 NVIDIA V olta GPUs and 4584 nodes on the Summit supercomputer with a 93.1% scaling efficiency, achieving peak and sustained half-precision rates of 1228 PF/s and 1207 PF/s. Index T erms --Stochastic PDEs, GANs, Deep Learning I. O VERVIEW A. Parameter estimation and uncertainty quantification for subsurface flow models Mathematical models of subsurface flow and transport are inherently uncertain because of the lack of data about the distribution of geological units, the distribution of hydrological properties (e.g., hydraulic conductivity) within each unit, and initial and boundary conditions. Here, we focus on parameter-ization and uncertainty quantification (UQ) in the subsurface flow model at the Department of Energy's Hanford Site, one of the most contaminated sites in the western hemisphere. During the Hanford Site's 60-plus years history, there have been more than 1000 individual sources of contaminants distributed over 200 square miles mostly along Columbia River [1]. Accurate subsurface flow models with rigorous UQ are necessary for assessing risks of the contaminants reaching the Columbia river and numerous wells used by agriculture and as sources of drinking water, as well as for the design of efficient remediation strategies. B. UQ with Stochastic Partial Differential Equations Uncertain initial and boundary conditions and model parameters render the governing model equations stochastic. In this context, UQ becomes equivalent to solving stochastic PDEs (SPDEs). Forward solution of SPDEs requires that all model parameters as well as the initial/boundary conditions are prescribed either deterministically or stochastically, which is not possible unless experimental data are available to provide additional information for critical parameters, e.g. the field conductivity.

Quantum computing (QC) has gained popularity due to its unique capabilities that are quite different from that of classical computers in terms of speed and methods of operations. This paper proposes hybrid models and methods that effectively leverage the complementary strengths of deterministic algorithms and QC techniques to overcome combinatorial complexity for solving large-scale mixed-integer programming problems. Four applicatio ns, namely the molecular conformation problem, job-shop scheduling problem, manufacturin g cell formation problem, and the vehicle routing problem, are specifically addressed. Large-scale instances of these application problems across multiple scales ranging from molecular design t o logistics optimization are computationally challenging for deterministic optimization algorithms on classical computers. To address the computational challenges, hybrid QC-based algorithms are proposed and extensive computational experimental results are presented to demonstrate their applicability and efficiency. The proposed QC-based solution strategies enjoy high computatio nal efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.

Organised by Fira de Barcelona in partnership with the IIC (Industrial Internet Consortium), the IOTSWC will be held from 29th to 31st October in Gran Via venue in the framework of Barcelona Industry Week. It's the largest international event devoted to the industrial internet and, throughout its history, has complemented its offering with other converging technologies that are accelerating the digital transformation of sectors such as transport, manufacturing, healthcare, energy, utilities, construction, infrastructure, retail, and agriculture, among others. In addition, IOTSWC 2019 will have international and institutional pavilions. In this regard, the presence of stands from Greece, Austria, Germany, Spain, Romania, Sweden, France, Baviera, Catalonia and Barcelona has already been confirmed; these will contribute a large number of companies to the event, many of them SMEs and start-ups linked to the IoT ecosystem. A new feature of this year's fair will be a specific area called IoT Solutions.Font, which will provide visibility for start-ups with original and innovative IoT, Artificial Intelligence, and Blockchain based products and services that have already been tested in the market and with potential for internationalisation.

I'm not qualified as a Financial Advisor, and there's a non-zero chance that my forecasts are ALL WRONG. I'm gonna do a Chicago Wheat fair value forecast for this first post. There's a ton of data from USDA, and it could get quite overwhelming. Breaking the data into smaller bits could help interpret the data more meaningfully for grain traders. I have no clue, looking at these numbers alone without referencing historical precedence.