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NASA NTRS1996

South Atlantic Anomaly Entry and Exit as Measured by the X-Ray Timing Explorer

The Rossi X-ray Timing Explorer (RXTE) carries instruments that must switch off high voltages (HV) when passing through the South Atlantic Anomaly (SAA). The High Energy X-ray Timing Experiment (HEXTE) contains a particle monitor that detects the increased particle flux associated with the SAA and autonomously reduces its voltage. The Proportional Counter Array (PCA) relies on uplinked predictions of SAA entry/exit times based on ephemeris data provided by the Flight Dynamics Facility. A third instrument, the All-Sky Monitor (ASM) also uses a predicted SAA model to reduce voltage when passing through the SAA. Data collected from the HEXTE particle monitor, as well as other instrument readings near the times of SAA entry/exit offer the potential for refining models of the boundaries of the SAA. The SAA has an increased particle flux which causes high rates of detection in the RXTE instruments designed to observe x-rays. The high counting rates could degrade the PCA if HV is not reduced during SAA passages. On the other hand, PCA downtime can be minimized and the science return can be optimized by having the best possible model of the SAA boundary. Thus, the PCA team planned an extensive effort during in-orbit checkout to utilize both the HEXTE particle monitor data and instrument counting rates to refine the model of the SAA boundary. The times of SAA entry and exit are compared with the definitive epemeris to determine the precise location (latitude and longitude) of the SAA boundary. Over time, the SAA and its perimeter were mapped. The RXTE Science Operations Center is continuously working to feed back the results of this effort into the science scheduling process, improving the SAA model as it affects the RXTE instruments, thus obtaining more accurate estimates of the SAA entry/exit times.

Smith, Evan↗

NASA NTRS1996

Flight Test of a Propulsion-Based Emergency Control System on the MD-11 Airplane with Emphasis on the Lateral Axis

A large, civilian, multi-engine transport MD-11 airplane control system was recently modified to perform as an emergency backup controller using engine thrust only. The emergency backup system, referred to as the propulsion-controlled aircraft (PCA) system, would be used if a major primary flight control system fails. To allow for longitudinal and lateral-directional control, the PCA system requires at least two engines and is implemented through software modifications. A flight-test program was conducted to evaluate the PCA system high-altitude flying characteristics and to demonstrate its capacity to perform safe landings. The cruise flight conditions, several low approaches and one landing without any aerodynamic flight control surface movement, were demonstrated. This paper presents results that show satisfactory performance of the PCA system in the longitudinal axis. Test results indicate that the lateral-directional axis of the system performed well at high attitude but was sluggish and prone to thermal upsets during landing approaches. Flight-test experiences and test techniques are also discussed with emphasis on the lateral-directional axis because of the difficulties encountered in flight test.

Burken, John J.↗

NASA NTRS1998

Using Engine Thrust for Emergency Flight Control: MD-11 and B-747 Results

With modern digital control systems, using engine thrust for emergency flight control to supplement or replace failed aircraft normal flight controls has become a practical consideration. The NASA Dryden Flight Research Center has developed a propulsion-controlled aircraft (PCA) system in which computer-controlled engine thrust provides emergency flight control. An F-15 and an MD-11 airplane have been landed without using any flight control surfaces. Preliminary studies have also been conducted that show that engines on only one wing can provide some flight control capability if the lateral center of gravity can be shifted toward the side of the airplane that has the operating engine(s). Simulator tests of several airplanes with no flight control surfaces operating and all engines out on the left wing have all shown positive control capability within the available range of lateral center-of-gravity offset. Propulsion-controlled aircraft systems that can operate without modifications to engine control systems, thus allowing PCA technology to be installed on less capable airplanes or at low cost, are also desirable. Further studies have examined simplified 'PCA Lite' and 'PCA Ultralite' concepts in which thrust control is provided by existing systems such as auto-throttles or a combination of existing systems and manual pilot control.

Burcham, Frank W., Jr.↗

NASA NTRS2003

Enough Is Enough

The spacecraft was nearly integrated and had passed some of its early mechanical and electrical testing. One of its instruments, the Proportional Counter Array (PCA), had a gas leak in one of the five proportional counter modules that made up the array. The science division where the instrument was being developed wanted a gas replenishment system added to assure the PCA would last for the entire mission. Adding a gas replenishment system would mean interrupting spacecraft integration and testing; developing a new subsystem and integrating it onto the spacecraft; modifying all the PCA modules; including a complex integration of the instrument onto the spacecraft; and implementing a more complex performance and environmental test process. It was the wrong answer because it made a simple design more complex and added little value to the mission at a major cost in time and dollars. Our mission couldn't afford the additional budget and schedule risks. XTE was the latest of a long line of projects being managed by my Explorer Program Office, but it was unique in being the first project we had agreed to do for a fixed price. NASA HQ agreed, in return, to provide us with the funding profile we needed to make it happen. We were both trying to break the unhealthy spiral in the Explorer program that saw current missions overrunning and pushing subsequent missions downstream to the point where their science was becoming marginal. The science community was upset and wanted better performance from NASA. I summarized my arguments to the director. The Engineering Directorate had taken responsibility for the spacecraft development when we established XTE as an in-house project at Goddard Space Flight Center, and also was supporting the PCA development. "It adds complexity," I reiterated. "It's a significant cost impact for only a marginal reliability increase". His response was music to my ears, "Jim, I won't stand in your way, but you'll have to convince the scientists and engineers."

Barrowman, James↗

NASA NTRS2006

Aircraft Control Using Engine Thrust: A History of Learning TOC Real-Time

A history of learning the operation of Throttles Only Control (TOC) to control an aircraft in real time using engine thrust is shown. The topics include: 1) Past TOC Accidents/Incidents; 2) 1972: DC-10 American Airlines; 3) May 1974: USAF B-52H; 4) April 1975: USAF C-5A; 5) April 1975: USAF C-5A; 6) 1981: USAF B-52G; 7) August 1985: JAL 123 B-747; 8) JAL 123 Survivor Story; 9) JAL 123 Investigation Findings; 10) July 1989: UAL 232 DC-10; 11) UAL 232 DC-10; 12) Eastwind 517 B-737; 13) November 2003: DHL A-300; 14) Historically, TOC has saved lives; 15) Automated Throttles-Only Control; 16) PCA Project; 17) Propulsion-Controlled Aircraft; 18) MD-11 PCA System and Flight Test Envelope; 19) MD-11 Simulation, PCA ILS-Soupled Landing Dispersion; 20) Throttles-Only Pitch and Roll Control Power; 21) PCA in Commercial Fleet; 22) Fall 2005: PCAR Project; 23) PCAR Background - TOC; and 24) PCAR Background - TOC.

Cole, Jennifer H.↗

NASA NTRS2008

Derivation of Boundary Manikins: A Principal Component Analysis

When designing any human-system interface, it is critical to provide realistic anthropometry to properly represent how a person fits within a given space. This study aimed to identify a minimum number of boundary manikins or representative models of subjects anthropometry from a target population, which would realistically represent the population. The boundary manikin anthropometry was derived using, Principal Component Analysis (PCA). PCA is a statistical approach to reduce a multi-dimensional dataset using eigenvectors and eigenvalues. The measurements used in the PCA were identified as those measurements critical for suit and cockpit design. The PCA yielded a total of 26 manikins per gender, as well as their anthropometry from the target population. Reduction techniques were implemented to reduce this number further with a final result of 20 female and 22 male subjects. The anthropometry of the boundary manikins was then be used to create 3D digital models (to be discussed in subsequent papers) intended for use by designers to test components of their space suit design, to verify that the requirements specified in the Human Systems Integration Requirements (HSIR) document are met. The end-goal is to allow for designers to generate suits which accommodate the diverse anthropometry of the user population.

Young, Karen↗

NASA NTRS2008

Generation of Boundary Manikin Anthropometry

The purpose of this study was to develop 3D digital boundary manikins that are representative of the anthropometry of a unique population. These digital manikins can be used by designers to verify and validate that the components of the spacesuit design satisfy the requirements specified in the Human Systems Integration Requirements (HSIR) document. Currently, the HSIR requires the suit to accommodate the 1st percentile American female to the 99th percentile American male. The manikin anthropometry was derived using two methods: Principal Component Analysis (PCA) and Whole Body Posture Based Analysis (WBPBA). PCA is a statistical method for reducing a multidimensional data set by using eigenvectors and eigenvalues. The goal is to create a reduced data set that encapsulates the majority of the variation in the population. WBPBA is a multivariate analytical approach that was developed by the Anthropometry and Biomechanics Facility (ABF) to identify the extremes of the population for a given body posture. WBPBA is a simulation-based method that finds extremes in a population based on anthropometry and posture whereas PCA is based solely on anthropometry. Both methods yield a list of subjects and their anthropometry from the target population; PCA resulted in 20 female and 22 male subjects anthropometry and WBPBA resulted in 7 subjects' anthropometry representing the extreme subjects in the target population. The subjects anthropometry is then used to 'morph' a baseline digital scan of a person with the same body type to create a 3D digital model that can be used as a tool for designers, the details of which will be discussed in subsequent papers.

Young, Karen S.↗

NASA NTRS2007

Emergency Flight Control of a Twin-Jet Commercial Aircraft using Manual Throttle Manipulation

The Department of Homeland Security (DHS) created the PCAR (Propulsion-Controlled Aircraft Recovery) project in 2005 to mitigate the ManPADS (man-portable air defense systems) threat to the commercial aircraft fleet with near-term, low-cost proven technology. Such an attack could potentially cause a major FCS (flight control system) malfunction or other critical system failure onboard the aircraft, despite the extreme reliability of current systems. For the situations in which nominal flight controls are lost or degraded, engine thrust may be the only remaining means for emergency flight control [ref 1]. A computer-controlled thrust system, known as propulsion-controlled aircraft (PCA), was developed in the mid 1990s with NASA, McDonnell Douglas and Honeywell. PCA's major accomplishment was a demonstration of an automatic landing capability using only engine thrust [ref 11. Despite these promising results, no production aircraft have been equipped with a PCA system, due primarily to the modifications required for implementation. A minimally invasive option is TOC (throttles-only control), which uses the same control principles as PCA, but requires absolutely no hardware, software or other aircraft modifications. TOC is pure piloting technique, and has historically been utilized several times by flight crews, both military and civilian, in emergency situations stemming from a loss of conventional control. Since the 1990s, engineers at NASA Dryden Flight Research Center (DFRC) have studied TOC, in both simulation and flight, for emergency flight control with test pilots in numerous configurations. In general, it was shown that TOC was effective on certain aircraft for making a survivable landing. DHS sponsored both NASA Dryden Flight Research Center (Edwards, CA) and United Airlines (Denver, Colorado) to conduct a flight and simulation study of the TOC characteristics of a twin-jet commercial transport, and assess the ability of a crew to control an aircraft down to a survivable runway landing using TOC. The PCAR project objective was a set of pilot procedures for operation of a specific aircraft without hydraulics that (a) have been validated in both simulation and flight by relevant personnel, and (b) mesh well with existing commercial operations, maintenance, and training at a minimum cost. As a result of this study, a procedure has been developed to assist a crew in making a survivable landing using TOC. In a simulation environment, line pilots with little or no previous TOC experience performed survivable runway landings after a few practice TOC approaches. In-flight evaluations put line pilots in a simulated emergency situation where TOC was used to recover the aircraft, maneuver to a landing site, and perform an approach down to 200 feet AGL. The results of this research, including pilot observations, procedure comments, recommendations, future work and lessons learned, will he discussed. Flight data and video footage of TOC approaches may also be shown.

Cole, Jennifer H.↗

NASA NTRS2012

Application of Principal Component Analysis to NIR Spectra of Phyllosilicates: A Technique for Identifying Phyllosilicates on Mars

Orbital near-infrared (NIR) reflectance spectra of the martian surface from the OMEGA and CRISM instruments have identified a variety of phyllosilicates in Noachian terrains. The types of phyllosilicates present on Mars have important implications for the aqueous environments in which they formed, and, thus, for recognizing locales that may have been habitable. Current identifications of phyllosilicates from martian NIR data are based on the positions of spectral absorptions relative to laboratory data of well-characterized samples and from spectral ratios; however, some phyllosilicates can be difficult to distinguish from one another with these methods (i.e. illite vs. muscovite). Here we employ a multivariate statistical technique, principal component analysis (PCA), to differentiate between spectrally similar phyllosilicate minerals. PCA is commonly used in a variety of industries (pharmaceutical, agricultural, viticultural) to discriminate between samples. Previous work using PCA to analyze raw NIR reflectance data from mineral mixtures has shown that this is a viable technique for identifying mineral types, abundances, and particle sizes. Here, we evaluate PCA of second-derivative NIR reflectance data as a method for classifying phyllosilicates and test whether this method can be used to identify phyllosilicates on Mars.

Rampe, E. B.↗

NASA NTRS2015

Principal Component Analysis of Thermographic Data

Principal Component Analysis (PCA) has been shown effective for reducing thermographic NDE data. While a reliable technique for enhancing the visibility of defects in thermal data, PCA can be computationally intense and time consuming when applied to the large data sets typical in thermography. Additionally, PCA can experience problems when very large defects are present (defects that dominate the field-of-view), since the calculation of the eigenvectors is now governed by the presence of the defect, not the "good" material. To increase the processing speed and to minimize the negative effects of large defects, an alternative method of PCA is being pursued where a fixed set of eigenvectors, generated from an analytic model of the thermal response of the material under examination, is used to process the thermal data from composite materials. This method has been applied for characterization of flaws.

Winfree, William P.↗

NASA NTRS2015

Assessing Wetland Hydroperiod and Soil Moisture With Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

Primary Goal: Assist with the evaluation and measuring of wetlands hydroperiod at the PlumBrook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: 1) Show the relative length of hydroperiod using available remote sensing datasets 2) Date linked table of wetlands extent over time for all feasible non-forested wetlands 3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables 4) A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment 5) A MTRI style report summarizing year 2 results. This report serves as a descriptive summary of our completion of these our deliverables. Additionally, two formal meetings were held with Larry Liou and Amanda Sprinzl to provide project updates and receive direction on outputs. These were held on 2/26/15 and 9/17/15 at the Plum Brook Station. Principal Component Analysis (PCA) is a multivariate statistical technique used to identify dominant spatial and temporal backscatter signatures. PCA reduces the information contained in the temporal dataset to the first few new Principal Component (PC) images. Some advantages of PCA include the ability to filter out temporal autocorrelation and reduce speckle to the higher order PC images. A PCA was performed using ERDAS Imagine on a time series of PALSAR dates. Hydroperiod maps were created by separating the PALSAR dates into two date ranges, 2006-2008 and 2010, and performing an unsupervised classification on the PCAs.

Brooks, Colin↗

NASA NTRS2019

A New Discrete Wavelength BUV Algorithm for Consistent Volcanic SO2 Retrievals from Multiple Satellite Missions

This paper describes a new discrete wavelength algorithm developed for retrieving volcanic sulfur dioxide (SO2) vertical column density (VCD) from UV observing satellites. The Multi-Satellite SO2 algorithm (MS_SO2) simultaneously retrieves column densities of sulfur dioxide, ozone, and Lambertian effective reflectivity (LER) and its spectral dependence. It is used operationally to process measurements from the heritage Total Ozone Mapping Spectrometer (TOMS) onboard NASA's Nimbus-7 satellite (N7/TOMS: 1978-1993) and from the current Earth Polychromatic Imaging Camera (EPIC) onboard Deep Space Climate Observatory (DSCOVR: 2015-) from the Earth-Sun Lagrange (L1) orbit. Results from MS_SO2 algorithm for several volcanic cases were assessed using the more sensitive principal component analysis (PCA) algorithm. The PCA is an operational algorithm used by NASA to retrieve SO2 from hyperspectral UV spectrometers, such as the Ozone Monitoring Instrument (OMI) onboard NASA's Earth Observing System Aura satellite and Ozone Mapping and Profiling Suite (OMPS) onboard NASA-NOAA Suomi National Polar Partnership (SNPP) satellite. For this comparative study, the PCA algorithm was modified to use the discrete wavelengths of the Nimbus-7/TOMS instrument, described in Sect. S1 of the Supplement. Our results demonstrate good agreement between the two retrievals for the largest volcanic eruptions of the satellite era, such as the 1991 Pinatubo eruption. To estimate SO2 retrieval systematic uncertainties, we use radiative transfer simulations explicitly accounting for volcanic sulfate and ash aerosols. Our results suggest that the discrete-wavelength MS_SO2 algorithm, although less sensitive than hyperspectral PCA algorithm, can be adapted to retrieve volcanic SO2 VCDs from contemporary hyperspectral UV instruments, such as OMI and OMPS, to create consistent, multi-satellite, long-term volcanic SO2 climate data records.

Bradford L Fisher↗

NASA NTRS2020

MSL Telecom Automated Anomaly Detection

The Mars Science Laboratory (MSL) Telecom Operations Team at the Jet Propulsion Laboratory (JPL) has implemented a machine learning system in order to automate the anomaly detection process as a part of daily operations. Machine learning enables reliable detection of anomalies in Telecom-related telemetry and automated reporting of Telecom subsystem status, resulting in an 90% reduction in team workload and improved anomaly detection reliability. At present, machine learning methods are used to detect: 1. Anomalous long-term trends in telemetry data 2. Anomalous time-domain evolution of telemetry values Both types of anomalies pose their own unique challenges that are addressed in different ways. In the first case, long term trending of daily minima, maximum, and mean telemetry values in temperatures, currents, voltages, and radio frequency (RF) power levels is used in addition to hard threshold safety checks to look for changes in long-term equipment health and performance. Long-term trending methods allow for ordinary seasonal variations in these quantities caused by temperature changes over the course of the Martian year while allowing operators to determine whether current performance remains in line with historical values from previous years. Changes in long-term trends can provide important insights into the health and status of the rover's on-board systems as well as valuable early warning if subtle degradation begins to take hold. But while trending of daily statistics is valuable, it does not detect anomalies in the short-term time evolution of data over the course of minutes or hours during a day, and this task is handled with short-term shape analysis. Principal components analysis (PCA) has been found to provide robust detection of short-term anomalies, and several examples of the use of PCA to detect actual anomalous events will be provided here. In using PCA, we use both the percentage of explained variance and also a log likelihood test on the PCA expansion coefficients to flag telemetry data for human review. Previous work in the field of spacecraft anomaly detection includes [1] for MSL and [2] for some other JPL missions.

Mukai, Ryan↗

NASA NTRSDate not supplied

Derivation of Integrated Load Distributions from Resampled Computational Data

Principal component analysis (PCA) has been the center of many surrogate models used to characterize fluid flows in recent years. However, little work has been done to character- ize the uncertainty in the PCA transform itself and its effect on derived surrogate models. To explore the uncertainty, a typical interpolated surrogate model is constructed for a represen- tative aerodynamic body from computational data. The computational data is then resampled to explore the robustness of the PCA transformation. The variations of the model predictions during this resampling are analyzed to get a measure of confidence in the PCA transformation, which is then applied to the interpolated surrogate model to get uncertainty on integrated force predictions. An initial test case has been explored with promising results.

Principal Component Analysis↗

NASA NTRSDate not supplied

Derivation of Integrated Load Distributions from Resampled Computational Data

Computational Fluid Dynamics↗

DOE OSTI2025

In planta production of the nylon precursor beta-ketoadipate

Beta-ketoadipate (βKA) is an intermediate of the βKA pathway involved in the degradation of aromatic compounds in several bacteria and fungi. Beta-ketoadipate also represents a promising chemical for the manufacturing of performance-advantaged nylons. We established a strategy for the in planta synthesis of βKA via manipulation of the shikimate pathway and the expression of bacterial enzymes from the βKA pathway. Using Nicotiana benthamiana as a transient expression system, we demonstrated the efficient conversion of protocatechuate (PCA) to βKA when plastid-targeted bacterial-derived PCA 3,4-dioxygenase (PcaHG) and 3-carboxy-cis,cis-muconate cycloisomerase (PcaB) were co-expressed with 3-deoxy-D-arabinoheptulosonate 7-phosphate synthase (AroG) and 3-dehydroshikimate dehydratase (QsuB). This metabolic pathway was reconstituted in Arabidopsis by introducing a construct (pAtβKA) with stacked pcaG, pcaH, and pcaB genes into a PCA-overproducing genetic background that expresses AroG and QsuB (referred as QsuB-2). The resulting QsuB-2 x pAtβKA stable lines displayed βKA titers as high as 0.25 % on a dry weight basis in stems, along with a drastic reduction in lignin content and improvement of biomass saccharification efficiency compared to wild-type controls, and without any significant reduction in biomass yields. Using biomass sorghum as a potential crop for large-scale βKA production, techno-economic analysis indicated that βKA accumulated at titers of 0.25 % and 4 % on a dry weight basis could be competitively priced in the range of $2.04-34.49/kg and $0.47-2.12/kg, respectively, depending on the selling price of the residual biomass recovered after βKA extraction. This study lays the foundation for a more environmentally-friendly synthesis of βKA using plants as production hosts.

Kazaz, Sami↗

DOE OSTI2026

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗

DOE OSTI2025

Modeling MTS pyrolysis and SiC deposition kinetics using principal component analysis and neural networks

Accurate chemical kinetics modeling is crucial for improving the efficiency of chemical processing and synthesis of ceramic matrix composites. Detailed kinetic models are computationally expensive due to the large number of transported chemical species, while the simplified physics-based models, such as single-step global mechanisms, are efficient but often overlook key chemical intermediates and pathways. Recent deep learning approaches promise accurate and cost-effective models. Yet, they require additional closures for the transported nonlinear latent variables, complicating integration with existing solvers. In this work, we develop a hybrid linear—nonlinear reduced model for silicon carbide deposition from methyltrichlorosilane precursor by combining principal component analysis (PCA) and autoencoder (AE) neural network (NN) approaches. PCA is used to identify a smaller set of linear transport variables, enabling direct reuse of conventional transport solvers. NNs then reconstruct the full chemical state from these reduced variables. We demonstrate the method on a chemical vapor deposition reactor—comprising a gas-phase pyrolysis plug flow reactor and a heterogeneous surface reactor—over a wide range of temperatures, pressures, and residence times. Our PCA–AE model achieves high accuracy with only five transported scalars, achieving an eightfold cost reduction compared to detailed mechanisms, in both a priori (using data from the test set only) and a posteriori (coupled with a differential equation solver). In conclusion, notable errors arise primarily near training domain boundaries and for long residence times, indicating the need for domain shift indicators and better long-horizon predictions in future reduced chemistry model development.

autoencoder neural networks↗