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DOE OSTI2020

Emerging materials intelligence ecosystems propelled by machine learning

We report that the age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human-machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities. The materials research landscape is being transformed by the infusion of approaches based on machine learning. This Review discusses the emerging materials intelligence ecosystems and the potential of human-machine partnerships for fast and efficient virtual materials screening, development and discovery.

36 MATERIALS SCIENCE↗

NASA NTRS2004

Meeting the Deadline: Why, When and How

A normative system is defined as any set of interacting agents whose behavior can usefully be regarded as norm-directed. Most organizations, and more specifically institutions, fall under this definition. Interactions in these normative systems are regulated by normative templates that describe desired behavior in terms of deontic concepts (obligations, prohibitions and permissions), deadlines, violations and sanctions. Agreements between agents, and between an agent and the society, can then be specified by means of contracts. Contracts provide flexible but verifiable means to integrate society requirements and agent autonomy. and are an adequate means for the explicit specification of interactions. From the society perspective, it is important that these contracts adhere to the specifications described in the model of the organization. If we want to automate such verifications, we have to formalize the languages used for contracts and for the specification of organizations. The logic LCR is based on deontic temporal logic. LCR is an expressive language for describing interaction in multi-agent systems, including obligations with deadlines. Deadlines are important norms in most interactions between agents. Intuitively, a deadline states that an agent should perform an action before a certain point in time. The obligation to perform the action starts at the moment the deadline becomes active. E.g. when a contract is signed or approved. If the action is not performed in time a violation of the deadline occurs. It can be specified independently what measure has to be taken in this case. In this paper we investigate the deadline concept in more detail. The paper is organized as follows. Section 2 defines the variant of CTL we use. In section 3, we discuss the basic intuitions of deadlines. Section 4 presents a first intuitive formalization for deadlines. In section 5, we look at a more complex model for deadlines trying to catch some more practical aspects. Finally, in section 6 we present issues for future work and our conciusions.

Dignum, Frank↗

NASA NTRS2015

Check-Cases for Verification of 6-Degree-of-Freedom Flight Vehicle Simulations

The rise of innovative unmanned aeronautical systems and the emergence of commercial space activities have resulted in a number of relatively new aerospace organizations that are designing innovative systems and solutions. These organizations use a variety of commercial off-the-shelf and in-house-developed simulation and analysis tools including 6-degree-of-freedom (6-DOF) flight simulation tools. The increased affordability of computing capability has made highfidelity flight simulation practical for all participants. Verification of the tools' equations-of-motion and environment models (e.g., atmosphere, gravitation, and geodesy) is desirable to assure accuracy of results. However, aside from simple textbook examples, minimal verification data exists in open literature for 6-DOF flight simulation problems. This assessment compared multiple solution trajectories to a set of verification check-cases that covered atmospheric and exo-atmospheric (i.e., orbital) flight. Each scenario consisted of predefined flight vehicles, initial conditions, and maneuvers. These scenarios were implemented and executed in a variety of analytical and real-time simulation tools. This tool-set included simulation tools in a variety of programming languages based on modified flat-Earth, round- Earth, and rotating oblate spheroidal Earth geodesy and gravitation models, and independently derived equations-of-motion and propagation techniques. The resulting simulated parameter trajectories were compared by over-plotting and difference-plotting to yield a family of solutions. In total, seven simulation tools were exercised.

Murri, Daniel G.↗

NASA NTRS2009

Proceedings of the First NASA Formal Methods Symposium

Topics covered include: Model Checking - My 27-Year Quest to Overcome the State Explosion Problem; Applying Formal Methods to NASA Projects: Transition from Research to Practice; TLA+: Whence, Wherefore, and Whither; Formal Methods Applications in Air Transportation; Theorem Proving in Intel Hardware Design; Building a Formal Model of a Human-Interactive System: Insights into the Integration of Formal Methods and Human Factors Engineering; Model Checking for Autonomic Systems Specified with ASSL; A Game-Theoretic Approach to Branching Time Abstract-Check-Refine Process; Software Model Checking Without Source Code; Generalized Abstract Symbolic Summaries; A Comparative Study of Randomized Constraint Solvers for Random-Symbolic Testing; Component-Oriented Behavior Extraction for Autonomic System Design; Automated Verification of Design Patterns with LePUS3; A Module Language for Typing by Contracts; From Goal-Oriented Requirements to Event-B Specifications; Introduction of Virtualization Technology to Multi-Process Model Checking; Comparing Techniques for Certified Static Analysis; Towards a Framework for Generating Tests to Satisfy Complex Code Coverage in Java Pathfinder; jFuzz: A Concolic Whitebox Fuzzer for Java; Machine-Checkable Timed CSP; Stochastic Formal Correctness of Numerical Algorithms; Deductive Verification of Cryptographic Software; Coloured Petri Net Refinement Specification and Correctness Proof with Coq; Modeling Guidelines for Code Generation in the Railway Signaling Context; Tactical Synthesis Of Efficient Global Search Algorithms; Towards Co-Engineering Communicating Autonomous Cyber-Physical Systems; and Formal Methods for Automated Diagnosis of Autosub 6000.

Denney, Ewen↗

NASA NTRSDate not supplied

Semantic Search with Sentence-BERT for Design Information Retrieval

Managing and referencing design knowledge is a critical activity in the design process. However, reliably retrieving useful knowledge can be a frustrating experience for users of knowledge management systems due to inherent limitations of standard keyword-based searches. In this research, we consider the task of retrieving relevant lessons learned from the NASA Lessons Learned Information System (LLIS). To this end, we apply a state-of-the-art natural language processing (NLP) technique for information retrieval (IR): semantic search with sentence-BERT, which is a modification of a Bidirectional Encoder Representations from Transformers (BERT) model that uses siamese and triplet network architectures to obtain semantically meaningful sentence embeddings. While the pre-trained sBERT model performs well out-of-the-box, we further fine-tune the model on data from the LLIS so that it learns on design engineering-relevant vocabulary. We quantify the improvement in query results using both standard sBERT and fine-tuned sBERT over a keyword search. Our use case throughout the paper is to use queries related to specific requirements from a NASA project. Fine tuning the sBERT model on LLIS data yields a mean average precision (MAP) of 0.807 on queries based on information needs from a real NASA project. Results indicate that applying state-of-the-art natural language processing techniques, especially when finetuned using engineering data, to design information retrieval tasks shows significant promise in modernizing design knowledge management systems.

Hannah S. Walsh↗

NASA NTRS1992

Approximate reasoning using terminological models

Term Subsumption Systems (TSS) form a knowledge-representation scheme in AI that can express the defining characteristics of concepts through a formal language that has a well-defined semantics and incorporates a reasoning mechanism that can deduce whether one concept subsumes another. However, TSS's have very limited ability to deal with the issue of uncertainty in knowledge bases. The objective of this research is to address issues in combining approximate reasoning with term subsumption systems. To do this, we have extended an existing AI architecture (CLASP) that is built on the top of a term subsumption system (LOOM). First, the assertional component of LOOM has been extended for asserting and representing uncertain propositions. Second, we have extended the pattern matcher of CLASP for plausible rule-based inferences. Third, an approximate reasoning model has been added to facilitate various kinds of approximate reasoning. And finally, the issue of inconsistency in truth values due to inheritance is addressed using justification of those values. This architecture enhances the reasoning capabilities of expert systems by providing support for reasoning under uncertainty using knowledge captured in TSS. Also, as definitional knowledge is explicit and separate from heuristic knowledge for plausible inferences, the maintainability of expert systems could be improved.

Yen, John↗

NASA NTRS2011

The Time Evolution of Eta Carinae's Colliding Winds

We report new HST/STIS observations that map the high-ionization forbidden line emission in the inner arc second of Eta Car, the first that fully image the extended wind-wind interaction region of the massive colliding wind binary. These observations were obtained after the 2009.0 periastron at orbital phases 0.084, 0.163, and 0.323 of the 5.54-year spectroscopic cycle. We analyze the variations in brightness and morphology of the emission, and find that blue-shifted emission (-400 to -200 km/s is symmetric and elongated along the northeast-southwest axis, while the red-shifted emission (+ 100 to +200 km/s) is asymmetric and extends to the north-northwest. Comparison to synthetic images generated from a 3-D dynamical model strengthens the 3-D orbital orientation found by Madura et al. (2011), with an inclination i = 138 deg, argument of periapsis w = 270 deg, and an orbital axis that is aligned at the same P A on the sky as the symmetry axis of the Homunculus, 312 deg. We discuss the potential that these and future mappings have for constraining the stellar parameters of the companion star and the long-term variability of the system. Plain-Language Abstract: With HST, we resolved the interacting winds of the binary, Eta Carinae. With a 3-D model, we find the binary orbit axis is aligned to the Homunculus axis. This suggests a connection between the binary and Homunculus ejection mechanism.

Gull, Theodore R.↗

NASA NTRS2018

Stochastic Reduced Order Models with Python (SROMPy)

Stochastic Reduced Order Models with Python (SROMPy) is a software package developed to enable user-friendly utilization of the stochastic reduced order model (SROM) approach for uncertainty quantification. A SROM is a low dimensional, discrete approximation to a random quantity that enables efficient and non-intrusive stochastic computations. With SROMPy, a user can easily generate a SROM to approximate a random variable or vector described by several different types of probability distributions using the Python programming language. Once a SROM is constructed, the software can be used to propagate uncertainty through a user-defined computational model to estimate statistics of a given quantity of interest. This report is meant to introduce the SROMPy module and brie y demonstrate its capabilities. A simple example of a spring-mass system with a random input is included to illustrate the practicality of the SROM approach to uncertainty quantification and relative ease of applying it with SROMPy. The example includes a comparison with a solution obtained using classical Monte Carlo simulation, demonstrating the similarities and advantages of using the SROM approach.

Warner, James E.↗

NASA NTRS1989

Adjustable impedance, force feedback and command language aids for telerobotics (parts 1-4 of an 8-part MIT progress report)

Projects recently completed or in progress at MIT Man-Machine Systems Laboratory are summarized. (1) A 2-part impedance network model of a single degree of freedom remote manipulation system is presented in which a human operator at the master port interacts with a task object at the slave port in a remote location is presented. (2) The extension of the predictor concept to include force feedback and dynamic modeling of the manipulator and the environment is addressed. (3) A system was constructed to infer intent from the operator's commands and the teleoperation context, and generalize this information to interpret future commands. (4) A command language system is being designed that is robust, easy to learn, and has more natural man-machine communication. A general telerobot problem selected as an important command language context is finding a collision-free path for a robot.

Sheridan, Thomas B.↗

NASA NTRSDate not supplied

Simplifying Requirements Formalization for Resource-Constrained Mission-Critical Software

Developing critical software requires adherence to rigorous software development practices, such as formal requirement specification and verification. Despite their importance, such practices are often considered as complex and challenging tasks that require a strong formal methods background. In this paper, we present our work on simplifying the formal requirements specification experience for resource-constrained mission critical software through the use of structured natural language. To this end, we connect NASA’s FRET, a formal requirement elicitation and authoring tool with the Shelley model checking framework for MicroPython code. We report our experience on using these tools to specify requirements and analyze code from the NASA Ames PHALANX exploration concept.

PHALANX exploration concept↗

NASA NTRS2005

Effects of ongoing task context and target typicality on prospective memory performance: the importance of associative cueing

Two experiments examined whether prospective memory performance is influenced by contextual cues. In our automatic activation model, any information available at encoding and retrieval should aid recall of the prospective task. The first experiment demonstrated an effect of the ongoing task context; performance was better when information about the ongoing task present at retrieval was available at encoding. Performance was also improved by a strong association between the prospective memory target as it was presented at retrieval and the intention as it was encoded. Experiment 2 demonstrated boundary conditions of the ongoing task context effect, which implicate the association between the ongoing and prospective tasks formed at encoding as the source of the context effect. The results of this study are consistent with predictions based on automatic activation of intentions.

Cues↗

DOE OSTI2024

Emulator-Based Bayesian Calibration of the CISNET Colorectal Cancer Models

Purpose To calibrate Cancer Intervention and Surveillance Modeling Network (CISNET)'s SimCRC, MISCAN-Colon, and CRC-SPIN simulation models of the natural history colorectal cancer (CRC) with an emulator-based Bayesian algorithm and internally validate the model-predicted outcomes to calibration targets.Methods We used Latin hypercube sampling to sample up to 50,000 parameter sets for each CISNET-CRC model and generated the corresponding outputs. We trained multilayer perceptron artificial neural networks (ANNs) as emulators using the input and output samples for each CISNET-CRC model. We selected ANN structures with corresponding hyperparameters (i.e., number of hidden layers, nodes, activation functions, epochs, and optimizer) that minimize the predicted mean square error on the validation sample. We implemented the ANN emulators in a probabilistic programming language and calibrated the input parameters with Hamiltonian Monte Carlo-based algorithms to obtain the joint posterior distributions of the CISNET-CRC models' parameters. We internally validated each calibrated emulator by comparing the model-predicted posterior outputs against the calibration targets.Results The optimal ANN for SimCRC had 4 hidden layers and 360 hidden nodes, MISCAN-Colon had 4 hidden layers and 114 hidden nodes, and CRC-SPIN had 1 hidden layer and 140 hidden nodes. The total time for training and calibrating the emulators was 7.3, 4.0, and 0.66 h for SimCRC, MISCAN-Colon, and CRC-SPIN, respectively. The mean of the model-predicted outputs fell within the 95% confidence intervals of the calibration targets in 98 of 110 for SimCRC, 65 of 93 for MISCAN, and 31 of 41 targets for CRC-SPIN.Conclusions Using ANN emulators is a practical solution to reduce the computational burden and complexity for Bayesian calibration of individual-level simulation models used for policy analysis, such as the CISNET CRC models. In this work, we present a step-by-step guide to constructing emulators for calibrating 3 realistic CRC individual-level models using a Bayesian approach.

artificial neural networks↗

NASA NTRS2016

Exposing Hidden Parts of the SE Process: MBSE Patterns and Tools for Tracking and Traceability

An interesting benefit of applying Model-Based Systems Engineering (MBSE) is that the rigor and coordination intrinsic to MBSE forces us to apply Systems Engineering to our own traditional activities, processes, and products, which results in richer, more expressive models, more powerful reasoning, and a clearer and more effective Systems Engineering (SE) process. Our MBSE frameworks and languages contain semantic richness sufficient to describe our systems at any particular point in time, often with an emphasis on the description of the system at major milestones. This is unarguably a real asset. However, when we apply MBSE in service of missions that are in development, rapidly evolving, of a larger scale, and where interpersonal communication is a critical part of the design process, we discover that our frameworks and languages are still not quite rich enough to enable us to ask the kinds of questions and get the kinds of answers we want in order to address the concerns of day to day work. This paper will discuss some patterns and tools we have developed to help address some of the not-always-explicit SE concerns that we have identified through our MBSE work. Particularly, this paper will discuss flexible yet practical methods for defining and capturing maturity, workflow, and agreement traceability within our system models, extensible ways to perform and track model audits, and ways to report and interact with this knowledge in the context of MBSE applied to support NASA’s Europa Project.

Jackson, Maddalena↗

NASA NTRS2016

Exposing Hidden Parts of the SE Process: MBSE Patterns and Tools for Tracking and Traceability

Jackson, Maddalena↗

NASA NTRS2000

IRIS Product Recommendations

This report presents the Applied Meteorology Unit's (AMU) evaluation of SIGMET Inc.'s Integrated Radar Information System (IRIS) Product Generator and recommendations for products emphasizing lightning and microburst tools. The IRIS Product Generator processes radar reflectivity data from the Weather Surveillance Radar, model 74C (WSR-74C), located on Patrick Air Force Base. The IRIS System was upgraded from version 6.12 to version 7.05 in late December 1999. A statistical analysis of atmospheric temperature variability over the Cape Canaveral Air Force Station (CCAFS) Weather Station provided guidance for the configuration of radar products that provide information on the mixed-phase (liquid and ice) region of clouds, between 0 C and -20 C. Mixed-phase processes at these temperatures are physically linked to electrification and the genesis of severe weather within convectively generated clouds. Day-to-day variations in the atmospheric temperature profile are of sufficient magnitude to warrant periodic reconfiguration of radar products intended for the interpretation of lightning and microburst potential of convectively generated clouds. The AMU also examined the radar volume-scan strategy to determine the scales of vertical gaps within the altitude range of the 0 C to -20 C isotherms over the Kennedy Space Center (KSC)/CCAFS area. This report present's two objective strategies for designing volume scans and proposes a modified scan strategy that reduces the average vertical gap by 37% as a means for improving radar observations of cloud characteristics in the critical 0 C to -20 C layer. The AMU recommends a total of 18 products, including 11 products that require use of the IRIS programming language and the IRIS User Product Insert feature. Included is a cell trends product and display, modeled after the WSR-88D cell trends display in use by the National Weather Service.

Short, David A.↗

DOE OSTI2022

Development and Commercialization of an IDAES-Based Power Plant Performance Monitoring and Optimization System

DOE and NETL have created an advanced, open-source computational platform through the Institute for the Design of Advanced Energy Systems (IDAES). The IDAES platform is a very extensive modeling environment that can be used for a broad range of power plant and process applications. MapEx Software is developing and commercializing a software application that makes it easier to set-up and run IDAES-based analyses. The MapEx-developed software application will replace the need for custom Python language coding with a user-friendly graphical user interface where the user can construct a flowsheet diagram of the IDAES model by inserting icons representing the plant equipment onto the screen. This application will make the implementation of modeling and optimization of existing fossil-fired power plants more straight-forward and less time-consuming. The effort focuses on performance monitoring and optimization of plant operations for the existing coal-fired power plant fleet but is built on a structure that allows expansion into the broad range of applications where IDAES methods may be applied.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2025

Applying Gaussian Process Machine Learning and Modern Probabilistic Programming to Satellite Data to Infer CO 2 Emissions

Satellite data provides essential insights into the spatiotemporal distribution of CO 2 concentrations. However, many atmospheric inverse models fail to adequately incorporate the spatial and temporal correlations inherent in satellite observations and often lack rigorous methods for estimating parameters like spatial length scales. We introduce an inference model that processes the spatiotemporal covariance in satellite data and estimates hyperparameters such as covariance length scales. Our approach uses the Gaussian process (GP) machine learning (ML) and modern probabilistic programming languages (PPLs) to perform atmospheric inversions of emissions from satellite data. We develop a GP ML inversion system based on modern PPLs and the GEOS-Chem chemical transport model, simulating atmospheric CO 2 concentrations corresponding to the Orbiting Carbon Observatory-2/3 (OCO-2/3) data for July 2020. In our supervised learning framework, we treat the GEOS-Chem simulated data set as the target, with predictors derived by scaling the target with sector-specific factors hidden from the GP machine. Our results show that the GP model, combined with GPU-enabled PPLs, effectively retrieves true emission scaling factors and infers noise levels concealed within the data. This suggests that our method could be applied over larger areas with more complex covariance structures, enabling comprehensive analysis of the spatiotemporal patterns observed in OCO-2/3 and similar satellite data sets.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1986

Simulation of the space station information system in Ada

The Flexible Ada Simulation Tool (FAST) is a discrete event simulation language which is written in Ada. FAST has been used to simulate a number of options for ground data distribution of Space Station payload data. The fact that Ada language is used for implementation has allowed a number of useful interactive features to be built into FAST and has facilitated quick enhancement of its capabilities to support new modeling requirements. General simulation concepts are discussed, and how these concepts are implemented in FAST. The FAST design is discussed, and it is pointed out how the used of the Ada language enabled the development of some significant advantages over classical FORTRAN based simulation languages. The advantages discussed are in the areas of efficiency, ease of debugging, and ease of integrating user code. The specific Ada language features which enable these advances are discussed.

Spiegel, James R.↗