Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “hypothesis learning”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

matsim-agents v1.0

matsim-agents is a multi-agent AI framework for atomistic materials simulation and discovery. It orchestrates large language models (LLMs), machine-learned interatomic potentials (MLIPs), and DFT codes into a single agentic loop running on laptops and DOE leadership-class supercomputers. MULTI-AGENT ORCHESTRATION A LangGraph state machine with three nodes: a Planner that converts a natural-language research objective into structured tasks; an Executor that dispatches atomistic tools and loops until the queue is empty; and an Analyst that summarizes results into a human-readable report. State is checkpointed after every step and human-in-the-loop gates can be inserted at any edge. HYPOTHESIS-DRIVEN DISCOVERY CHAT An interactive REPL (matsim-agents chat) that couples LLM dialogue with atomistic simulation. Chemical formulas are automatically detected in conversation turns and trigger a full crystal-phase exploration: structure generation → relaxation → stability scoring → result injection back into the conversation, creating a closed hypothesis-refinement loop. CRYSTAL PHASE ENUMERATION Given a composition, the phase explorer enumerates prototypes by stoichiometry: elemental (fcc/bcc/hcp/sc/diamond), binary 1:1 (rocksalt/CsCl/zincblende/ wurtzite/fluorite/rutile), ternary 1:1:3 (cubic perovskite), ternary 1:2:4 (perovskite + spinel), quaternary 1:1:2:6 (Fm-3m double perovskite). 2-D prototypes (graphene, h-BN, MoS2 2H/1T) and multilayer stacking are also supported via --include-2d and --num-layers. SUPERCELL GENERATION AND SITE DECORATION Auto-tiling to a minimum atom count (--min-atoms), explicit NxNxN tiling (--supercell), symmetry-distinct site decorations (--n-orderings), and isotropic lattice-scale sweeps (--lattice-scales) for volume bracketing. MLFF RELAXATION AND STABILITY SCORING HydraGNN (multi-headed GNN) drives structure relaxation via ASE with FIRE, BFGS, or BFGSLineSearch. Stability output: delta-E/atom ranking across phases and a max-residual-force dynamical-stability proxy. Other MLIPs (MACE, NequIP, Orb) can be plugged in through the same interface. DFT BACKENDS Quantum ESPRESSO pw.x and VASP 6.6 are first-class labellers. Both have validated GPU builds and SLURM/PBS launchers for three DOE platforms: Frontier (AMD MI250X, ROCm), Aurora (Intel PVC, oneAPI), Perlmutter (NVIDIA A100, CUDA). QE produces ~100 binaries (pw.x, ph.x, epw.x, ...). VASP supports scf, relax, vc-relax, and vc-relax-shape run types. ACTIVE-LEARNING LOOP matsim-agents al run CONFIG.yaml drives an iterative HydraGNN-DFT loop: MD generates candidates → ensemble/MC-dropout uncertainty selects the most informative → DFT labels them in parallel inside one allocation → dataset grows → HydraGNN retrains → repeat. DFT backend is a single YAML toggle (dft.backend: vasp | qe). LLM-generated seed structures are supported (no curated POSCAR library needed). Config uses ${VAR}, ${VAR:-default}, ${VAR:?msg} shell-style substitution for cross-user/cross-site portability. LLM BACKENDS Ollama (local, default), vLLM (HPC multi-GPU serving), OpenAI, Anthropic, HuggingFace Transformers+Accelerate. Selected at runtime via flag or env var with no code changes. HPC PORTABILITY Same Python entry points run on Frontier (ROCm 7.2), Aurora (oneAPI), and Perlmutter (CUDA 12). DFT and ML stacks are never co-loaded in the same shell; they couple through the scheduler and filesystem. Advanced multi-node launchers (serve, discovery-chat, single-relaxation, active-learning, QE warm-start) are provided for all three platforms. CODABENCH COMPETITION BUNDLE A self-contained benchmark: 159 atomistic test structures across 11 material classes, 5 tasks (formation energy, forces, ML relaxation, AI-DFT relaxation, phase stability ranking), public/private leaderboard split (30/70), and four ready-to-run baselines: MACE-MP-0, HydraGNN, UMA, AllScAIP.

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗
DOE OSTI2021

A science paradigm shift is needed for Earth and Environmental Systems Sciences (EESS) to integrate Knowledge-Guided Artificial Intelligence (KGAI) and lead new EESS-KGAI theories

The focal area of this white paper is learning from complex data through the use of AI techniques and AI-derived model components. Specifically, we advocate for research programs to develop knowledge-guided AI (KGAI) in the Earth and Environmental Systems sciences (EESS) as a basic research paradigm that is separate from (but supports) any specific Earth system model, modeling components, and modeling workflows, and even separates from specific hypothesis-driven questions about individual Earth system processes.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗
NASA NTRSDate not supplied

Feasibility Study of Distributed Decision-Making on the Edge for Urban Air Mobility

The Concept of Operations for Urban Air Mobility (UAM) put forward by FAA, NASA, and several industry stakeholders acknowledges the diversity and complexity in UAM operations and, thereby, envisions a federated architecture for UAM management. In this architecture, the decision-making is distributed to a set of service providers who collectively manage the shared airspace usage by different stakeholders. This notionally brings autonomy closer to the UAM businesses and encourages to explore the feasibility of decision making on the very edge, which is the topic of the presented research. This paper reports research conducted on the hypothesis based on which the residual compute capability onboard smart unmanned aerial systems (UASs) is utilized to build situational awareness and resolve conflicts by passive and active coordination among multiple UASs, thereby implementing a layer of distributed autonomy in UAM. Key features of the edge-computing approach involve inter-UAS information exchange, independent assessment of own flight and environmental conditions, and estimation of other UASs’ flight preferences, incorporating machine learning techniques in the last two. Parallel computing on portable graphics processing unit (GPU) enables the machine learning workflow on the edge. A custom-built 3D simulator is used to evaluate the efficacy of the distributed decision-making on the edge. Each edge node, representing a smart UAS, connects to the simulator from a remote location and independently controls the behavior of the corresponding virtual asset in the simulator, analogous to participants in an online multi-player game. The presented edge-computing-based distributed decision-making framework is envisioned to pave the way for collective mobility of autonomous air vehicles in the future shared airspace, while allowing the inclusion of the business preferences of the UAS operators within allowed regulatory limits.

Edge computing↗
NASA NTRS1988

Blindness in designing intelligent systems

New investigations of the foundations of artificial intelligence are challenging the hypothesis that problem solving is the cornerstone of intelligence. New distinctions among three domains of concern for humans--description, action, and commitment--have revealed that the design process for programmable machines, such as expert systems, is based on descriptions of actions and induces blindness to nonanalytic action and commitment. Design processes focusing in the domain of description are likely to yield programs like burearcracies: rigid, obtuse, impersonal, and unable to adapt to changing circumstances. Systems that learn from their past actions, and systems that organize information for interpretation by human experts, are more likely to be successful in areas where expert systems have failed.

Denning, Peter J.↗
NASA NTRS2022

Observing Coronal Microscales and Their Connection With Mesoscales

Why the Sun has a tenuous upper atmosphere some 1000 times hotter than the photosphere is a fundamental open problem in space plasma physics despite decades of study. A leading hypothesis, supported by indirect evidence, is that in most of the corona heating is confined to narrow current sheets in which energy is dissipated despite the low large-scale resistivity of the coronal plasma. Although the kinetic scales of reconnection or wave heating are beyond remote observation, thermal structure on scales ≲100 km are expected to be produced by the primary heating mechanisms operating within a filamentary magnetic field. This white paper considers what could be learned from direct observations of coronal plasma on those scales and outlines a mission concept that is more fully described in a Heliophysics Mission Concept Study for the Coronal Microscale Observatory.

Douglas Rabin↗
DOE OSTI2023

ML-ACCEPT: Machine-Learning-enhanced Automated Circuit Configuration and Evaluation of Power Converters

This project, ML-ACCEPT, took a significant step toward achieving the first objective of the DIFFERENTIATE program: to help engineers to more rapidly and cost-effectively consider a wider range of more novel concepts before selecting an engineering-optimal architecture for high-fidelity detailed design and evaluation. Compared to existing methods, the ML-ACCEPT project investigated innovative technologies to make the design of power converters more cost-effective and time-efficient by i) integrating recent breakthroughs in ML, power electronics, simulation software, and optimization to research, develop, and developed a suite of ML-enhanced hypothesis generation tools for power converter design; and ii) facilitating the integration of the proposed software tools into existing power-converter design work-flows. Some existing tools for power-converter design have a certain level of intelligence to aid the design process, but to our knowledge none of them has ML-enhanced capability of automating the electrical circuit design process by selecting, configuring, and tailoring the individual components that enable available resources (e.g., high-voltage DC power) to be converted to the desired output (e.g., lower-voltage DC power with a desired voltage ripple), subject to application-specific (e.g., plasma generation and automotive applications) thermal and packaging considerations. The state-of-the-art circuit design of power converters is still heavily reliant on human experts to select the optimal topology and search for design parameters with human’s experience and intuitions, which can be very time-consuming, inefficient, and labor intensive. The team is composed of University of Michigan-Dearborn (UM-Dearborn), Lawrence Livermore National Laboratory (LLNL), Oak Ridge National Laboratory (ORNL), Modelon Inc., and lighthouse customers.

42 ENGINEERING↗
NASA NTRS2010

On-Orbit Prospective Echocardiography on International Space Station Crew

Introduction A prospective trial of echocardiography was conducted on of six crewmembers onboard the International Space Station. The main objective was to determine the efficacy of remotely guided tele-echocardiography, including just-in-time e-training methods and determine what "space normal" echocardiographic data is. Methods Each crewmember operator (n=6) had 2-hour preflight training. Baseline echocardiographic data were collected 55 to 167days preflight. Similar equipment was used in each 60-minute in-flight session (mean microgravity exposure - 114 days (34 -- 190)). On Orbit ultrasound operators used an e-learning system within 24h of these sessions. Expert assistance was provided using ultrasound video downlink and two-way voice. Testing was repeated 5 to 16 days after landing. Separate ANOVA was used on each echocardiographic variable (n=33). Within each ANOVA, three tests were made: a) effect of mission phase (preflight, in-flight, post flight); b) effect of echo technician (two technicians independently analyzed the data); c) interaction between mission phase and technician. Results Nine rejections of the null hypothesis (mission phase or technician or both had no effect) were discovered and considered for follow up. Of these, six rejections were for significant technician effects, not as a result of space flight. Three rejections of the null hypothesis (Aortic Valve time velocity integral, Mitral E wave Velocity and heart rate) were attributable to space flight, however determined not to be clinically significant. No rejections were due to the interaction between technician and space flight. Conclusion No consistent clinically significant effects of long-duration space flight were seen in echocardiographic variables of the given group of subjects.

Hamilton, Douglas R.↗
NASA NTRS1990

Learning and diagnosing faults using neural networks

Neural networks have been employed for learning fault behavior from rocket engine simulator parameters and for diagnosing faults on the basis of the learned behavior. Two problems in applying neural networks to learning and diagnosing faults are (1) the complexity of the sensor data to fault mapping to be modeled by the neural network, which implies difficult and lengthy training procedures; and (2) the lack of sufficient training data to adequately represent the very large number of different types of faults which might occur. Methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Secondly, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented during training. These methods were tested on a task of fault diagnosis by employing rocket engine simulator data. Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.

Whitehead, Bruce A.↗
NASA NTRS2015

A Faster Triphosphorylation Ribozyme

In support of the RNA world hypothesis, previous studies identified trimetaphosphate (Tmp) as a plausible energy source for RNA world organisms. In one of these studies, catalytic RNAs (ribozymes) that catalyze the triphosphorylation of RNA 5'-hydroxyl groups using Tmp were obtained by in vitro selection. One ribozyme (TPR1) was analyzed in more detail. TPR1 catalyzes the triphosphorylation reaction to a rate of 0.013 min -1 under selection conditions (50 mM Tmp, 100 mM MgCl 2 , 22°C). To identify a triphosphorylation ribozyme that catalyzes faster triphosphorylation, and possibly learn about its secondary structure TPR1 was subjected to a doped selection. The resulting ribozyme, TPR1e, contains seven mutations relative to TPR1, displays a previously unidentified duplex that constrains the ribozyme's structure, and reacts at a 24-fold faster rate than the parent ribozyme. Under optimal conditions (150 mM Tmp, 650 mM MgCl 2 , 40°C), the triphosphorylation rate of TRP1e reaches 6.8 min -1 .

Gregory F. Dolan↗
NASA NTRS1997

Learnability of Red-Green Opponency

Lennie, Haake, and Williams found that in the lateral geniculate nucleus (LGN), parvocellular unit responses are consistent with the hypothesis that their input connectivity is blind to the difference between middle-wavelength-sensitive (MWS) and long-wavelength-sensitive (LWS) cones. Most of their cells have a total MWS input weight opposite in sign and similar in magnitude to their total LWS input weight. If these weights are exactly balanced, the construction of a red-green opponent system from such units is simple: such units need only be aligned so the signs of their outputs agree. Ahumada and Mulligan described an associative learning process which can accomplish this alignment. If the units are not balanced (carry some luminance information), the strong overlap between the MWS and LWS spectral responses can cause units to agree more on the basis of luminance, and the associative process fails to produce red-green opponency. The learning theory requires that the LGN units be nearly balanced (more strongly encode chromaticity than luminance) and quantitatively expresses the requirement: the principal component of the LGN outputs must be in the red-green rather than the luminance direction. We show that the cone weights of the monkey LGN cells measured by Derrington, Krauskopf, and Lennie can satisfy this learnability criterion even if the MWS spectral response is close to the LWS spectral response, simulating anomalous trichromacy. The learnability theory provides a source of visual system variation for explaining why different anomalous trichromats may make the same average anomaloscope match (same pigments), but have either narrow (good opponent learning) or wide (poor learning) ranges of acceptable matches.

Ahumada, Albert J., Jr.↗
DOE OSTI2020

Machine Learning Approaches for Nuclear Material Accounting Data from Irradiation and Reprocessing

We are currently exploring data analysis methods for their ability to strengthen the synthesis and evaluation of information generated within domestic and international safeguard regimes. Safeguard data typically includes rich heterogenous datasets amenable to advanced data analytics methods, such as machine learning. We have converted transactional data containing both numerical and categorical attributes from a domestic nuclear material control and accountability (NMC&A) system into a low-dimensional numerical structure via linear principal component analysis. This data representation allows for global structure discovery via cluster analysis, which can characterize the typical behavior of each of the primary types of transaction events. Furthermore, the structure of the top principal components captures the “typical behavior” of the data and is thus amenable to anomaly detection through statistical hypothesis testing. We explored this capability by generating erroneous permutations of the data and computing the Q-residual quantity increase associated with the information loss when these data are projected into the low-dimensional principal component analysis space representative of typical transactions.Future work will focus on identifying data transformations (e.g., graph networks) that more closely align with the inherent structure of these transactions to explain more salient information and disambiguate the underlying nuclear process—in this case, irradiation and reprocessing—from artifacts of NMC&A system transactional record keeping data.

Drescher, Adam↗
DOE OSTI2024

Odor exposure during imprinting periods increases odorant-specific sensitivity and receptor gene expression in coho salmon ( Oncorhynchus kisutch )

ABSTRACT Pacific salmon are well known for their homing migrations; juvenile salmon learn odors associated with their natal streams prior to seaward migration, and then use these retained odor memories to guide them back from oceanic feeding grounds to their river of origin to spawn several years later. This memory formation, termed olfactory imprinting, involves (at least in part) sensitization of the peripheral olfactory epithelium to specific odorants. We hypothesized that this change in peripheral sensitivity is due to exposure-dependent increases in the expression of odorant receptor (OR) proteins that are activated by specific odorants experienced during imprinting. To test this hypothesis, we exposed juvenile coho salmon, Oncorhynchus kisutch, to the basic amino acid odorant l-arginine during the parr–smolt transformation (PST), when imprinting occurs, and assessed sensitivity of the olfactory epithelium to this and other odorants. We then identified the coho salmon ortholog of a basic amino acid odorant receptor (BAAR) and determined the mRNA expression levels of this receptor and other transcripts representing different classes of OR families. Exposure to l-arginine during the PST resulted in increased sensitivity to that odorant and a specific increase in BAAR mRNA expression in the olfactory epithelium relative to other ORs. These results suggest that specific increases in ORs activated during imprinting may be an important component of home stream memory formation and this phenomenon may ultimately be useful as a marker of successful imprinting to assess management strategies and hatchery practices that may influence straying in salmon.

Dittman, Andrew H. (ORCID:000000016482359X)↗
NASA NTRS2003

The Meteoritic Component in Impact Deposits

This proposal requested support for a broad-based research program designed to understand the chemical and mineralogical record of accretion of extraterrestrial matter to the Earth. The primary goal of this research is to study the accretion history of the Earth, to understand how this accretion history reflects the long-term flux of comets, asteroids, and dust in the inner solar system and how this flux is related to the geological and biological history of the Earth. This goal is approached by seeking out the most significant projects that can be attacked utilizing the expertise of the PI and potential collaborators. The greatest expertise of the PI is the analysis of meteoritic components in terrestrial sediments. This proposal identifies three primary areas of research, involving impact events in the early Archean (3.2 Ga), the late Eocene (35 Ma) and the late Pliocene (2 Ma). In the early Archean we investigate sediments that contain the oldest recorded impacts on Earth. These are thick spherule beds, three of which were deposited within 20 m.y. If these are impact deposits the flux of objects to Earth at this time was much greater than predicted by current models. Earlier work used Cr isotopes to prove that one of these contain extraterrestrial matter, from a projectile with Cr isotopes similar to CV chondrites. We planned to expand this work to other spherule beds and to search for additional evidence of other impact events. With samples from D. Lowe (Stanford Univ.) the PI proposed to screen samples for high Ir and Cr so that appropriate samples can be provided to A. Shukolyukov for Cr-isotopic analyses. This work was expected to provide evidence that at least one interval in the early Archean was a period of intense bombardment and to characterize the composition of objects accreted. The late Eocene is also a period of intense bombardment with multiple spherule deposits and two large craters. Farley et al. (1998) demonstrated an increased (3)He flux to marine sediments that was attributed to an increase in interplanetary dust due to a shower of comets invading the inner solar system. We planned to detect a change in the Cr-isotopic composition in the flux of fine-grained extraterrestrial matter to ocean sediments. This would provide evidence for the comet shower hypothesis. We planned attempt to locate late Eocene impact deposits in a new high resolution, hi-latitude site that had the potential for excellent preservation and new information of the sources and effects of these impacts. Although most impacts on Earth occur in deep-ocean basins, only one such event is known - the late Pliocene impact of the Eltanin asteroid. The ejecta from this impact includes Ir- rich impact melt, spherules, and actual meteorites from the km-sized mesosiderite asteroid. Through a study of the ejecta, we can learn about the formation, distribution, alteration and preservation of Ir-rich deposits. We can also learn about meteorite survival during hypervelocity impacts and we can study pieces of a km-sized object to learn more about the mesosiderite parent body. Analyses of sediment cores and geophysical exploration of the impact site can further our understanding of the processes involved in deep-ocean impacts and potential effects on the terrestrial climate and biosphere. We planned analyses of this ejecta, a search for ejecta 5000 km distant from the impact area, and a new oceanographic expedition to study and sample the impact site. In addition to these three specific areas of research, the PI planned to remain flexible and available to exploit new opportunities presented by new discoveries, and to engage in new collaborations if other researchers require his expertise to develop new projects that fit within the objectives of the overall research program.

Kyte, Frank T.↗
NASA NTRS2005

Lessons Learned in the Livingstone 2 on Earth Observing One Flight Experiment

The Livingstone 2 (L2) model-based diagnosis software is a reusable diagnostic tool for monitoring complex systems. In 2004, L2 was integrated with the JPL Autonomous Sciencecraft Experiment (ASE) and deployed on-board Goddard's Earth Observing One (EO-1) remote sensing satellite, to monitor and diagnose the EO-1 space science instruments and imaging sequence. This paper reports on lessons learned from this flight experiment. The goals for this experiment, including validation of minimum success criteria and of a series of diagnostic scenarios, have all been successfully net. Long-term operations in space are on-going, as a test of the maturity of the system, with L2 performance remaining flawless. L2 has demonstrated the ability to track the state of the system during nominal operations, detect simulated abnormalities in operations and isolate failures to their root cause fault. Specific advances demonstrated include diagnosis of ambiguity groups rather than a single fault candidate; hypothesis revision given new sensor evidence about the state of the system; and the capability to check for faults in a dynamic system without having to wait until the system is quiescent. The major benefits of this advanced health management technology are to increase mission duration and reliability through intelligent fault protection, and robust autonomous operations with reduced dependency on supervisory operations from Earth. The work-load for operators will be reduced by telemetry of processed state-of-health information rather than raw data. The long-term vision is that of making diagnosis available to the onboard planner or executive, allowing autonomy software to re-plan in order to work around known component failures. For a system that is expected to evolve substantially over its lifetime, as for the International Space Station, the model-based approach has definite advantages over rule-based expert systems and limit-checking fault protection systems, as these do not scale well. The model-based approach facilitates reuse of the L2 diagnostic software; only the model of the system to be diagnosed and telemetry monitoring software has to be rebuilt for a new system or expanded for a growing system. The hierarchical L2 model supports modularity and expendability, and as such is suitable solution for integrated system health management as envisioned for systems-of-systems.

Hayden, Sandra C.↗
NASA NTRS2013

Regression Analysis of Top of Descent Location for Idle-thrust Descents

In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. The independent variables cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also recorded or computed post-operations. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajec- tory parameters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowl- edge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace. In particular, a model for TOD location that is linear in the independent variables would enable decision support tool human-machine interfaces for which a kinetic approach would be computationally too slow.

trajectory prediction↗
NASA NTRS1992

Symmetry as Bias: Rediscovering Special Relativity

This paper describes a rational reconstruction of Einstein's discovery of special relativity, validated through an implementation: the Erlanger program. Einstein's discovery of special relativity revolutionized both the content of physics and the research strategy used by theoretical physicists. This research strategy entails a mutual bootstrapping process between a hypothesis space for biases, defined through different postulated symmetries of the universe, and a hypothesis space for physical theories. The invariance principle mutually constrains these two spaces. The invariance principle enables detecting when an evolving physical theory becomes inconsistent with its bias, and also when the biases for theories describing different phenomena are inconsistent. Structural properties of the invariance principle facilitate generating a new bias when an inconsistency is detected. After a new bias is generated. this principle facilitates reformulating the old, inconsistent theory by treating the latter as a limiting approximation. The structural properties of the invariance principle can be suitably generalized to other types of biases to enable primal-dual learning.

Lowry, Michael R.↗