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NASA NTRS2005

Self-dissimilarity as a High Dimensional Complexity Measure

For many systems characterized as "complex" the patterns exhibited on different scales differ markedly from one another. For example the biomass distribution in a human body "looks very different" depending on the scale at which one examines it. Conversely, the patterns at different scales in "simple" systems (e.g., gases, mountains, crystals) vary little from one scale to another. Accordingly, the degrees of self-dissimilarity between the patterns of a system at various scales constitute a complexity "signature" of that system. Here we present a novel quantification of self-dissimilarity. This signature can, if desired, incorporate a novel information-theoretic measure of the distance between probability distributions that we derive here. Whatever distance measure is chosen, our quantification of self-dissimilarity can be measured for many kinds of real-world data. This allows comparisons of the complexity signatures of wholly different kinds of systems (e.g., systems involving information density in a digital computer vs. species densities in a rain-forest vs. capital density in an economy, etc.). Moreover, in contrast to many other suggested complexity measures, evaluating the self-dissimilarity of a system does not require one to already have a model of the system. These facts may allow self-dissimilarity signatures to be used a s the underlying observational variables of an eventual overarching theory relating all complex systems. To illustrate self-dissimilarity we present several numerical experiments. In particular, we show that underlying structure of the logistic map is picked out by the self-dissimilarity signature of time series produced by that map

Wolpert, David H.↗

NASA NTRS2006

Biologically-Inspired Concepts for Self-Management of Complexity

Inherent complexity in large-scale applications may be impossible to eliminate or even ameliorate despite a number of promising advances. In such cases, the complexity must be tolerated and managed. Such management may be beyond the abilities of humans, or require such overhead as to make management by humans unrealistic. A number of initiatives inspired by concepts in biology have arisen for self-management of complex systems. We present some ideas and techniques we have been experimenting with, inspired by lesser-known concepts in biology that show promise in protecting complex systems and represent a step towards self-management of complexity.

Sterritt, Roy↗

NASA NTRS2009

Distributed Trajectory Flexibility Preservation for Traffic Complexity Mitigation

The growing demand for air travel is increasing the need for mitigation of air traffic congestion and complexity problems, which are already at high levels. At the same time new information and automation technologies are enabling the distribution of tasks and decisions from the service providers to the users of the air traffic system, with potential capacity and cost benefits. This distribution of tasks and decisions raises the concern that independent user actions will decrease the predictability and increase the complexity of the traffic system, hence inhibiting and possibly reversing any potential benefits. In answer to this concern, the authors propose the introduction of decision-making metrics for preserving user trajectory flexibility. The hypothesis is that such metrics will make user actions naturally mitigate traffic complexity. In this paper, the impact of using these metrics on traffic complexity is investigated. The scenarios analyzed include aircraft in en route airspace with each aircraft meeting a required time of arrival in a one-hour time horizon while mitigating the risk of loss of separation with the other aircraft, thus preserving its trajectory flexibility. The experiments showed promising results in that the individual trajectory flexibility preservation induced self-separation and self-organization effects in the overall traffic situation. The effects were quantified using traffic complexity metrics based on Lyapunov exponents and traffic proximity.

Idris, Husni↗

NASA NTRS2009

Energetic Processing of Interstellar Ices: A Route to Complexity

More than 140 gas-phase molecules have been detected in the interstellar (IS) medium or in circumstellar environments including inorganics, organics, ions, and radicals. The significant abundance of large, complex organic molecules, and families of isomers in these regions makes the origin and formation history of these species the subject of debate. Observationally determined condensed-phase species are H2O, CO, CO2, NH3 and CH30H, with CH4, HCOOH, OCS, OCN-, H2CO and NH4(+) present at trace levels. These ices can undergo energetic processing with cosmic rays or far-UV photons to form larger complex organics with abundance levels that make them undetectable in icy mantles. Once warmed, however, it is likely that these complex species would enter the gas-phase where they might be detected by Herschel or Alma. Understanding the role of radiation chemistry and thermal processing of ices and identifying new products are the goals of our laboratory research. In the Cosmic lee Laboratory at NASA Goddard Space Plight Center, we can study both the photo-and radiation chemistries of ices from 8 -- 300 K. Using dear- and mid-IR spectroscopy we can follow the destruction of primary molecules and the formation of radicals and secondary products as a function of energetic processing. During warming we can monitor the trapping of species and the results of any thermal chemistry. An overview of recent and past work will focus on complex secondary radiation products from small condensed-phase IS species. Likely reactions include dimerization, isomerization, H-addition and H-elimination. Another focus of our work is the development of reaction schemes for the formation of complex molecules and the use of such schemes to predict new molecules awaiting detection by Herschel and Alma.

Moore, Marla H.↗

NASA NTRS2009

Complexity for Survival of Living Systems

A logical connection between the survivability of living systems and the complexity of their behavior (equivalently, mental complexity) has been established. This connection is an important intermediate result of continuing research on mathematical models that could constitute a unified representation of the evolution of both living and non-living systems. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the two most relevant being Characteristics of Dynamics of Intelligent Systems (NPO- 21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; and Self-Supervised Dynamical Systems (NPO- 30634) NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72. As used here, living systems is synonymous with active systems and intelligent systems. The quoted terms can signify artificial agents (e.g., suitably programmed computers) or natural biological systems ranging from single-cell organisms at one extreme to the whole of human society at the other extreme. One of the requirements that must be satisfied in mathematical modeling of living systems is reconciliation of evolution of life with the second law of thermodynamics. In the approach followed in this research, this reconciliation is effected by means of a model, inspired partly by quantum mechanics, in which the quantum potential is replaced with an information potential. The model captures the most fundamental property of life - the ability to evolve from disorder to order without any external interference. The model incorporates the equations of classical dynamics, including Newton s equations of motion and equations for random components caused by uncertainties in initial conditions and by Langevin forces. The equations of classical dynamics are coupled with corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces that are gradients of the information potential, which, in turn, is a function of the probability densities. The probability densities are associated with mental images both self-image and nonself images (images of external objects that can include other agents). The evolution of the probability densities represents mental dynamics. Then the interaction between the physical and metal aspects of behavior is implemented by feedback from mental to motor dynamics, as represented by the aforementioned fictitious forces. The interaction of a system with its self and nonself images affords unlimited capacity for increase of complexity. There is a biological basis for this model of mental dynamics in the discovery of mirror neurons that learn by imitation. The levels of complexity attained by use of this model match those observed in living systems. To establish a mechanism for increasing the complexity of dynamics of an active system, the model enables exploitation of a chain of reflections exemplified by questions of the form, "What do you think that I think that you think...?" Mathematically, each level of reflection is represented in the form of an attractor performing the corresponding level of abstraction with more details removed from higher levels. The model can be used to describe the behaviors, not only of biological systems, but also of ecological, social, and economics ones.

Zak, Michail↗

NASA NTRS2009

Analysis of Trajectory Flexibility Preservation Impact on Traffic Complexity

The growing demand for air travel is increasing the need for mitigation of air traffic congestion and complexity problems, which are already at high levels. At the same time new information and automation technologies are enabling the distribution of tasks and decisions from the service providers to the users of the air traffic system, with potential capacity and cost benefits. This distribution of tasks and decisions raises the concern that independent user actions will decrease the predictability and increase the complexity of the traffic system, hence inhibiting and possibly reversing any potential benefits. In answer to this concern, the authors proposed the introduction of decision-making metrics for preserving user trajectory flexibility. The hypothesis is that such metrics will make user actions naturally mitigate traffic complexity. In this paper, the impact of using these metrics on traffic complexity is investigated. The scenarios analyzed include aircraft in en route airspace with each aircraft meeting a required time of arrival in a one-hour time horizon while mitigating the risk of loss of separation with the other aircraft, thus preserving its trajectory flexibility. The experiments showed promising results in that the individual trajectory flexibility preservation induced self-separation and self-organization effects in the overall traffic situation. The effects were quantified using traffic complexity metrics, namely dynamic density indicators, which indicated that using the flexibility metrics reduced aircraft density and the potential of loss of separation.

Idris, Husni↗

NASA NTRS2009

A Complexity Metric for Automated Separation

A metric is proposed to characterize airspace complexity with respect to an automated separation assurance function. The Maneuver Option metric is a function of the number of conflict-free trajectory change options the automated separation assurance function is able to identify for each aircraft in the airspace at a given time. By aggregating the metric for all aircraft in a region of airspace, a measure of the instantaneous complexity of the airspace is produced. A six-hour simulation of Fort Worth Center air traffic was conducted to assess the metric. Results showed aircraft were twice as likely to be constrained in the vertical dimension than the horizontal one. By application of this metric, situations found to be most complex were those where level overflights and descending arrivals passed through or merged into an arrival stream. The metric identified high complexity regions that correlate well with current air traffic control operations. The Maneuver Option metric did not correlate with traffic count alone, a result consistent with complexity metrics for human-controlled airspace.

Aweiss, Arwa↗

NASA NTRS2012

Discovery of a Plains Caldera Complex and Extinct Lava Lake in Arabia Terra, Mars: Implications for the Discovery of Additional Highland Volcanic Source Regions

Several irregularly shaped topographic depressions occur near the dichotomy boundary in northern Arabia Terra, Mars. The geomorphology of these features suggests that they formed by collapse, opposed to meteor impact. At least one depression (approx.55 by 85 km) displays geologic features indicating a complex, multi-stage collapse history. Features within and around the collapse structure indicate volcanic processes. The complex occurs within Hesperian ridged plains of likely volcanic origin and displays no crater rim or evidence for ejecta. Instead the depression consists of a series of circumferential graben and down-dropped blocks which also display upper surfaces similar to ridged plain lavas. Large blocks within the depression are tilted towards the crater center, and display graben that appear to have originally been linked with circumferential graben outside of the complex related to earlier collapse events. A nearly 700 m high mound exists along a graben within the complex that might be a vent. The deepest depression displays two sets of nearly continuous terraces, which we interpret as high-stands of a drained lava lake. These features appear similar to the black ledge described during the Kilauea Iki eruption in 1959. A lacustrine origin for the terraces seems unlikely because of the paucity of channels found in or around the depression that could be linked to aqueous surface processes. In addition, there is no obvious evidence for lacustrine sediments within the basin. Together with the presence of significant faulting that is indicative of collapse we conclude that this crater complex represents a large caldera formed in the Late Noachian to Early Hesperian. Other linear and irregular depressions in the region also might be linked to ancient volcanism. If that hypothesis is correct, it suggests that northern Arabia Terra could contain a large, previously unrecognized highland igneous province. Evacuation of magma via explosive and effusive activity produced localized collapse, might have contributed to nearby ridged plains, and pyroclastic materials erupted from these vents might have supplied sediments in fretted terrain and other deposits. The recognition of volcanoes within Arabia Terra expands the known extent of Noachian-Hesperian volcanism to cover much of the preserved martian highland crust.

Bleacher, Jacob↗

NASA NTRS2012

Prediction of Traffic Complexity and Controller Workload in Mixed Equipage NextGen Environments

Controller workload is a key factor in limiting en route air traffic capacity. Past efforts to quantify and predict workload have resulted in identifying objective metrics that correlate well with subjective workload ratings during current air traffic control operations. Although these metrics provide a reasonable statistical fit to existing data, they do not provide a good mechanism for estimating controller workload for future air traffic concepts and environments that make different assumptions about automation, enabling technologies, and controller tasks. One such future environment is characterized by en route airspace with a mixture of aircraft equipped with and without Data Communications (Data Comm). In this environment, aircraft with Data Comm will impact controller workload less than aircraft requiring voice communication, altering the close correlation between aircraft count and controller workload that exists in current air traffic operations. This paper outlines a new trajectory-based complexity (TBX) calculation that was presented to controllers during a human-in-the-loop simulation. The results showed that TBX accurately estimated the workload in a mixed Data Comm equipage environment and the resulting complexity values were understood and readily interpreted by the controllers. The complexity was represented as a "modified aircraft account" that weighted different complexity factors and summed them in such a way that the controllers could effectively treat them as aircraft count. The factors were also relatively easy to tune without an extensive data set. The results showed that the TBX approach is well suited for presenting traffic complexity in future air traffic environments.

Lee, Paul U.↗

NASA NTRS2016

Performance Analysis of a Hardware Implemented Complex Signal Kurtosis Radio-Frequency Interference Detector

Radio-frequency interference (RFI) is a known problem for passive remote sensing as evidenced in the L-band radiometers SMOS, Aquarius and more recently, SMAP. Various algorithms have been developed and implemented on SMAP to improve science measurements. This was achieved by the use of a digital microwave radiometer. RFI mitigation becomes more challenging for microwave radiometers operating at higher frequencies in shared allocations. At higher frequencies larger bandwidths are also desirable for lower measurement noise further adding to processing challenges. This work focuses on finding improved RFI mitigation techniques that will be effective at additional frequencies and at higher bandwidths. To aid the development and testing of applicable detection and mitigation techniques, a wide-band RFI algorithm testing environment has been developed using the Reconfigurable Open Architecture Computing Hardware System (ROACH) built by the Collaboration for Astronomy Signal Processing and Electronics Research (CASPER) Group. The testing environment also consists of various test equipment used to reproduce typical signals that a radiometer may see including those with and without RFI. The testing environment permits quick evaluations of RFI mitigation algorithms as well as show that they are implementable in hardware. The algorithm implemented is a complex signal kurtosis detector which was modeled and simulated. The complex signal kurtosis detector showed improved performance over the real kurtosis detector under certain conditions. The real kurtosis is implemented on SMAP at 24 MHz bandwidth. The complex signal kurtosis algorithm was then implemented in hardware at 200 MHz bandwidth using the ROACH. In this work, performance of the complex signal kurtosis and the real signal kurtosis are compared. Performance evaluations and comparisons in both simulation as well as experimental hardware implementations were done with the use of receiver operating characteristic (ROC) curves. The complex kurtosis algorithm has the potential to reduce data rate due to onboard processing in addition to improving RFI detection performance.

microwaves↗

NASA NTRS2019

A Limited Habitable Zone for Complex Life

The habitable zone (HZ) is commonly defined as the range of distances from a host star within which liquid water, a key requirement for life, may exist on a planet's surface. Substantially more CO2 than present in Earth's modern atmosphere is required to maintain clement temperatures for most of the HZ, with several bars required at the outer edge. However, most complex aerobic life on Earth is limited by CO2 concentrations of just fractions of a bar. At the same time, most exoplanets in the traditional HZ reside in proximity to M dwarfs, which are more numerous than Sun-like G dwarfs but are predicted to promote greater abundances of gases that can be toxic in the atmospheres of orbiting planets, such as carbon monoxide (CO). Here we show that the HZ for complex aerobic life is likely limited relative to that for microbial life. We use a 1D radiative-convective climate and photochemical models to circumscribe a Habitable Zone for Complex Life (HZCL) based on known toxicity limits for a range of organisms as a proof of concept. We find that for CO2 tolerances of 0.01, 0.1, and 1 bar, the HZCL is only 21%, 32%, and 50% as wide as the conventional HZ for a Sun-like star, and that CO concentrations may limit some complex life throughout the entire HZ of the coolest M dwarfs. These results cast new light on the likely distribution of complex life in the universe and have important ramifications for the search for exoplanet biosignatures and technosignatures.

Schwieterman, Edward W.↗

NASA NTRS2020

Analyzing Changes in the Complexity of Climate in the Last Four Decades Using MERRA-2 Radiation Data

The energy balance of the Earth is controlled by the shortwave and longwave radiation emitted to space. Changes in the thermodynamic state of the system over time afect climate and are noticeable when viewing the system as a whole. In this paper, we study the changes in the complexity of climate in the last four decades using data from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). First, we study the complexity of the shortwave and longwave radiation felds independently using Approximate Entropy and Sample Entropy, observing that the rate of complexity change is faster for shortwave radiation. Then, we study the causality of those changes using Transfer Entropy to capture the non-linear dynamics of climate, showing that the changes are mainly driven by the variations in shortwave radiation. The observed behavior of climatic complexity could be explained by the changes in cloud amount, and we research that possibility by investigating its evolution from a complexity perspective using data from the International Satellite Cloud Climatology Project (ISCCP).

Delgado-Bonal, Alfonso↗

NASA NTRS2021

Human Performance of Novice Schedulers for Complex Spaceflight Operations Timelines

Objective: Investigate the effects on human performance as a function of scheduling task complexity for novice schedulers creating spaceflight timelines. Background: Future astronauts will be expected to self-schedule, yet will not be experts in creating timelines that meet complex constraints inherent to spaceflight operations. Method: Conducted a within-subject experiment to measure scheduling efficiency, effectiveness, workload and situation awareness while varying scheduling task complexity factors, namely number of constraints and types of constraints. Results: Fifteen participants completed various scheduling problems. Performance differences were identified between the independent variables. There was a main effect due to the number of constraints and type of constraint for efficiency, effectiveness, and workload. Significant interactions were observed in situation awareness and workload for certain types of constraints. Results also suggest that a lower number of constraints may be manageable by novice schedulers when compared to scheduling activities without constraints. Conclusion: Results suggest that novice schedulers performance decreases with high number of constraints and future scheduling aids may have to be targeted to type of constraint. Application: Knowledge on the effect of scheduling task complexity will help design scheduling systems that will enable self-scheduling for future astronauts. It will also inform other domains that conduct complex scheduling, such as nursing and manufacturing.

scheduling↗

NASA NTRS2022

On the Importance of Investigating CME Complexity Evolution During Interplanetary Propagation

This perspective paper brings to light the need for comprehensive studies on the evolution of interplanetary coronal mass ejection (ICME) complexity during propagation. To date, few studies of ICME complexity exist. Here, we define ICME complexity and associated changes in complexity, describe recent works and their limitations, and outline key science questions that need to be tackled. Fundamental research on ICME complexity changes from the solar corona to 1 AU and beyond is critical to our physical understanding of the evolution and interaction of transients in the inner heliosphere. Furthermore, a comprehensive understanding of such changes is required to understand the space weather impact of ICMEs at different heliospheric locations and to improve on predictive space weather models.

CME↗

NASA NTRSDate not supplied

From Research to Reality -- Challenges and Opportunities in Complex System Design

The scope and scale of our interconnected society requires that we view the world through a complex system lens, where numerous parts interact, and emergent behaviors are the norm. Understanding complex systems is critical for decision-making and policy development in domains such as ecological systems, financial markets, supply chains, and global transportation systems, where decision-makers need reliable information to predict the impact of decisions that may play out over decades. Traditional approaches to the research that produces this information are often insufficient, where hypotheses are tested in an isolated environment, and where the results may not carry over to the integrated system. There are a multitude of advancements in system engineering, artificial intelligence, and test and evaluation that are emerging to meet the challenge. Approaches such as agile development, model-based system engineering, design of experiments, large language models, and formal ontologies are providing ways to manage complexity and increase our collective ability to make the changes that we want to see in the world. In this talk I will provide a few examples related to the architecture of the National Airspace System (NAS), where we have investigated the use of large language models, basic formal ontology, and applied category theory to help researchers and system engineers be more effective in this complex design space. Bio: Dr. Ian Levitt’s current research focus is on the complex evolution of the National Airspace System. Prior to joining NASA in 2020, he was with the FAA leading international standards and national laboratory development for the agency. Dr. Levitt earned his PhD in mathematics from Rutgers University in 2009. His mission is to promote a healthy and continuous transformation of society through open information and cooperation.

Ian Levitt↗

DOE OSTI2012

Heat Transfer in Complex Fluids

Amongst the most important constitutive relations in Mechanics, when characterizing the behavior of complex materials, one can identify the stress tensor T, the heat flux vector q (related to heat conduction) and the radiant heating (related to the radiation term in the energy equation). Of course, the expression 'complex materials' is not new. In fact, at least since the publication of the paper by Rivlin & Ericksen (1955), who discussed fluids of complexity (Truesdell & Noll, 1992), to the recently published books (Deshpande et al., 2010), the term complex fluids refers in general to fluid-like materials whose response, namely the stress tensor, is 'non-linear' in some fashion. This non-linearity can manifest itself in variety of forms such as memory effects, yield stress, creep or relaxation, normal-stress differences, etc. The emphasis in this chapter, while focusing on the constitutive modeling of complex fluids, is on granular materials (such as coal) and non-linear fluids (such as coal-slurries). One of the main areas of interest in energy related processes, such as power plants, atomization, alternative fuels, etc., is the use of slurries, specifically coal-water or coal-oil slurries, as the primary fuel. Some studies indicate that the viscosity of coal-water mixtures depends not only on the volume fraction of solids, and the mean size and the size distribution of the coal, but also on the shear rate, since the slurry behaves as shear-rate dependent fluid. There are also studies which indicate that preheating the fuel results in better performance, and as a result of such heating, the viscosity changes. Constitutive modeling of these non-linear fluids, commonly referred to as non-Newtonian fluids, has received much attention. Most of the naturally occurring and synthetic fluids are non-linear fluids, for example, polymer melts, suspensions, blood, coal-water slurries, drilling fluids, mud, etc. It should be noted that sometimes these fluids show Newtonian (linear) behavior for a given range of parameters or geometries; there are many empirical or semi-empirical constitutive equations suggested for these fluids. There have also been many non-linear constitutive relations which have been derived based on the techniques of continuum mechanics. The non-linearities oftentimes appear due to higher gradient terms or time derivatives. When thermal and or chemical effects are also important, the (coupled) momentum and energy equations can give rise to a variety of interesting problems, such as instability, for example the phenomenon of double-diffusive convection in a fluid layer. In Conclusion, we have studied the flow of a compressible (density gradient type) non-linear fluid down an inclined plane, subject to radiation boundary condition. The heat transfer is also considered where a source term, similar to the Arrhenius type reaction, is included. The non-dimensional forms of the equations are solved numerically and the competing effects of conduction, dissipation, heat generation and radiation are discussed. It is observed that the velocity increases rapidly in the region near the inclined surface and is slower in the region near the free surface. Since R{sub 7} is a measure of the heat generation due to chemical reaction, when the reaction is frozen (R{sub 7}=0.0) the temperature distributions would depend only on R{sub 1}, and R{sub 2}, representing the effects of the pressure force developed in the material due to the distribution, R{sub 3} and R{sub 4} viscous dissipation, R{sub 5} the normal stress coefficient, R{sub 6} the measure of the emissivity of the particles to the thermal conductivity, etc. When the flow is not frozen (RP{sub 7} > 0) the temperature inside the flow domain is much higher than those at the inclined and free surfaces. As a result, heat is transferred away from the flow toward both the inclined surface and the free surface with a rate that increases as R{sub 7} increases. For a given temperature, an increase in {zeta} implies that the activation energy is smaller and thus, the reaction rate is increased leading to an increase in the heat of the reaction. As a result the flow is chemically heated and its temperature increase. The results shown here indicate that for all values of {zeta} used the chemical effects are significant and the temperature is always higher than both the surface temperature and the free surface temperature. The heat transfer is always from the flow toward both the inclined surface and the free stream. It is also noticed that for all values of m chosen in this study, the temperature is higher than the surface and the free stream temperature. The heat transfer at the inclined surface and at the free stream increase slowly for negative values of m to about m=0.5, but it begins to significantly increase for m greater than 0.5.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2016

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

Current Deep Learning models use highly optimized convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers with a fairly simple layered network topology, i.e., highly connected layers, without intra-layer connections. Complex topologies have been proposed, but are intractable to train on current systems. Building the topologies of the deep learning network requires hand tuning, and implementing the network in hardware is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation. Due to input size limitations of current quantum computers we use the MNIST dataset for our evaluation. The results show the possibility of using the three architectures in tandem to explore complex deep learning networks that are untrainable using a von Neumann architecture. We show that a quantum computer can find high quality values of intra-layer connections and weights, while yielding a tractable time result as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware. This represents a new capability that is not feasible with current von Neumann architecture. It potentially enables the ability to solve very complicated problems unsolvable with current computing technologies.

Potok, Thomas E↗

DOE OSTI2021

Method of manufacturing a complex product by an additive process

A method of producing a complex product includes designing a three dimensional preform of the complex product, creating a three dimensional preform of the complex product using the model, depositing a material on the preform, and removing the preform to complete the complex product. In one embodiment the system provides a complex heat sink that can be used in heat dissipation in power electronics, light emitting diodes, and microchips.

Pascall, Andrew J.↗