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DOE OSTI2025

Flux Hypothesis for Odd Transport Phenomena

Onsager's regression hypothesis makes a fundamental connection between macroscopic transport phenomena and the average relaxation of spontaneous microscopic fluctuations. This relaxation, however, is agnostic to odd transport phenomena, in which fluxes run orthogonal to the gradients driving them. To account for odd transport, we generalize the regression hypothesis, postulating that macroscopic linear constitutive laws are, on average, obeyed by microscopic fluctuations, whether they contribute to relaxation or not. From this "flux hypothesis," Green-Kubo and reciprocal relations follow, elucidating the separate roles of broken time-reversal and parity symmetries underlying various odd transport coefficients. As an application, we derive and verify the Green-Kubo relation for odd collective diffusion in chiral active matter, first in an analytically tractable model and subsequently through molecular dynamics simulations of concentrated active spinners.

Hargus, Cory↗

DOE OSTI2016

Testing the Münch hypothesis of long distance phloem transport in plants

Long distance transport in plants occurs in sieve tubes of the phloem. The pressure flow hypothesis introduced by Ernst Münch in 1930 describes a mechanism of osmotically generated pressure differentials that are supposed to drive the movement of sugars and other solutes in the phloem, but this hypothesis has long faced major challenges. The key issue is whether the conductance of sieve tubes, including sieve plate pores, is sufficient to allow pressure flow. We show that with increasing distance between source and sink, sieve tube conductivity and turgor increases dramatically in Ipomoea nil. Our results provide strong support for the Münch hypothesis, while providing new tools for the investigation of one of the least understood plant tissues.

Life Sciences & Biomedicine - Other Topics↗

NASA NTRS1982

Pilot model hypothesis testing

The aircraft control time history predicted by the optimal control pilot model and actual pilot tracking data obtained from NASA Langley's differential maneuvering simulator (DMS) are analyzed. The analysis is performed using a hypothesis testing scheme modified to allow for changes in the true hypothesis. A finite number of pilot models, each with different hypothesized internal model representations of the aircraft dynamics, are constructed. The hypothesis testing scheme determines the relative probability that each pilot model best matches the DMS data. By observing the changes in probabilities, it is possible to determine when the pilot changes control strategy and which hypothesized pilot model best represent's the pilot's control behavior.

Broussard, J. R.↗

NASA NTRS1984

Tests of the lunar hypothesis

The concept that the Moon was fissioned from the Earth after core separation is the most readily testable hypothesis of lunar origin, since direct comparisons of lunar and terrestrial compositions can be made. Differences found in such comparisons introduce so many ad hoc adjustments to the fission hypothesis that it becomes untestable. Further constraints may be obtained from attempting to date the volatile-refractory element fractionation. The combination of chemical and isotopic problems suggests that the fission hypothesis is no longer viable, and separate terrestrial and lunar accretion from a population of fractionated precursor planetesimals provides a more reasonable explanation.

Taylor, S. R.↗

NASA NTRS1995

On Using Taylor's Hypothesis for Three-Dimensional Mixing Layers

In the present study, errors in using Taylor's hypothesis to transform measurements obtained in a temporal (or phase) frame onto a spatial one were evaluated. For the first time, phase-averaged ('real') spanwise and streamwise vorticity data measured on a three-dimensional grid were compared directly to those obtained using Taylor's hypothesis. The results show that even the qualitative features of the spanwise and streamwise vorticity distributions given by the two techniques can be very different. This is particularly true in the region of the spanwise roller pairing. The phase-averaged spanwise and streamwise peak vorticity levels given by Taylor's hypothesis are typically lower (by up to 40%) compared to the real measurements.

LeBoeuf, Richard L.↗

NASA NTRS1995

On Using Taylor's Hypothesis for Three-Dimensional Mixing Layers

LeBoeuf, Richard L.↗

NASA NTRS2018

Testing the Martian Methane from Cometary Debris Hypothesis: The Unusually Close 24 Jan 2018 Interaction Between Comet C/2007 H2 (Skiff) and Mars

In previous work we proposed a hypothesis wherein debris moving along cometary orbits interacting with Mars (e.g. meteor showers) may be responsible for transient local increases of methane observed in the martian atmosphere (henceforth 'the hypothesis' ). An examination of the literature of methane detections dating back to 1997 showed that each detection was made, at most, 16 days after an interaction between Mars and one of seven small bodies (six comets and the unusual object 5335 Damocles)[ibid]. Two observations of high-altitude, transient visible plumes on Mars also correlate with cometary interactions, one occurring on the same day as the plume observation and the second observation occurring three days afterwards, and with two of the same seven small bodies. The proposed mechanism for methane production is dissemination of carbon-rich cometary material on infall into Mars' atmosphere followed by methane production via UV photolysis, a process that has been observed in laboratory experiments. Given this set of observations it is necessary and indeed conducive to the scientific process to explore and robustly test the hypothesis.

Fries, M.↗

DOE OSTI2026

From Rules to Reasoning: A Survey of Large Language Model-Based Approaches to Scientific Hypothesis and Idea Generation

Scientific hypothesis generation represents a fundamental challenge in contemporary research due to exponentially expanding literature volumes and increasing disciplinary specialization. Large language models (LLMs) have emerged as transformative tools for automated scientific discovery, moving beyond traditional rule-based and literature-mining approaches. Four paradigmatic approaches define current LLM-driven hypothesis generation: direct prompting and fine-tuning methods, knowledge-enhanced frameworks integrating retrieval-augmented generation (RAG), multi-agent collaborative systems simulating research teams, and reasoning-focused approaches implementing cognitive architectures. Domain-specific applications demonstrate statistical equivalence to human expert performance in social psychology, experimental validation in biomedical research, and near-expert quality in astronomy. Evaluation methodologies encompass human expert assessment, LLM-as-judge frameworks, and comprehensive benchmarking systems. Technical challenges include hallucination management, knowledge integration limitations, and balancing novelty with feasibility. Future directions emphasize hybrid neural-symbolic architectures and sophisticated human-AI collaboration models for responsible scientific discovery acceleration.

AI-driven discovery↗

DOE OSTI2022

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

Machine learning is rapidly becoming an integral part of experimental physical discovery via automated and high-throughput synthesis, and active experiments in scattering and electron/probe microscopy. This, in turn, necessitates the development of active learning methods capable of exploring relevant parameter spaces with the smallest number of steps. In this work, an active learning approach based on conavigation of the hypothesis and experimental spaces is introduced. This is realized by combining the structured Gaussian processes containing probabilistic models of the possible system's behaviors (hypotheses) with reinforcement learning policy refinement (discovery). This approach closely resembles classical human-driven physical discovery, when several alternative hypotheses realized via models with adjustable parameters are tested during an experiment. This approach is demonstrated for exploring concentration-induced phase transitions in combinatorial libraries of Sm-doped BiFeO 3 using piezoresponse force microscopy, but it is straightforward to extend it to higher-dimensional parameter spaces and more complex physical problems once the experimental workflow and hypothesis generation are available.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials

Hypothesis-learning-driven automated scanning probe microscopy allows physical discovery during experiments, where the hypothesis learning algorithm implemented on an operating microscope identifies the physical mechanism behind the material’s response

36 MATERIALS SCIENCE↗

DOE OSTI2024

Insights into Prismatic Loop Formation in Irradiated Fe–Cr Alloys from Hypothesis-Driven Active Learning and Causal Analysis

Neutron and electron irradiation experimental studies conducted on body-centered cubic Fe and Fe–Cr alloys have established two prismatic dislocation loop populations, which have Burgers vectors of either a/2$\langle$111$\rangle$ or a$\langle$100$\rangle$. Here, the loop formation depends on factors such as dose (D), dose rate (D rt ), temperature (T), chromium content (Cr%), and other alloying elements. Hence, it is important to understand how irradiation-induced dislocation loops evolve conditional upon the loop characteristics, such as loop density (DD), average loop size d̅, and irradiation parameters (D, D rt , T, and irradiation type), which is still an active area of research. To understand these complex structure–property relationships, machine learning (ML) is employed in a three-step approach. This includes imputing missing data with a k-nearest neighbor, generating functionalized features, and assessing feature importance with random forest classification and regression. Physics-based features are incorporated in a hypothesis-driven active learning scheme to overcome data unavailability challenges. Insights obtained from ML models (i) to categorize dislocation loop types, show the highest correlation with d̅; (ii) Log(DD), obtained through mathematical formulations involving D, Cr%, d̅, and T (e.g., Log(DD) ~ D + exp(-Cr%) + 1/d̅ and log(DD) ~ D + exp(-Cr%) + 1/T). Hypothesis-driven active learning is able to predict Log(DD) in which the experimental date is not known. Causal models verify cause–effect relationships for dislocation loop classification and irradiation factors in FeCr alloys.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2011

A HYPOTHESIS FOR THE COLOR DIVERSITY OF THE KUIPER BELT

We propose a chemical and dynamical process to explain the surface colors of the Kuiper belt. In our hypothesis, the initial bulk compositions of the bodies themselves can be quite diverse-as is seen in comets-but the early surface compositions are set by volatile evaporation after the objects are formed. Strong gradients in surface composition, coupled with UV and particle irradiation, lead to the surface colors that are seen today. The objects formed in the inner part of the primordial belt retain only H{sub 2}O and CO{sub 2} as the major ice species on their surfaces. Irradiation of these species plausibly results in the dark neutrally colored centaurs and Kuiper belt objects (KBOs). Object formed further in the disk retain CH{sub 3}OH, which has been shown to lead to brighter redder surfaces after irradiation, as seen in the brighter redder centaurs and KBOs. Objects formed at the current location of the cold classical Kuiper belt uniquely retain NH{sub 3}, which has been shown to affect irradiation chemistry and could plausibly lead to the unique colors of these objects. We propose observational and experimental tests of this hypothesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2012

Using a Simple Binomial Model to Assess Improvement in Predictive Capability: Sequential Bayesian Inference, Hypothesis Testing, and Power Analysis

We present a Bayesian statistical methodology for identifying improvement in predictive simulations, including an analysis of the number of (presumably expensive) simulations that will need to be made in order to establish with a given level of confidence that an improvement has been observed. Our analysis assumes the ability to predict (or postdict) the same experiments with legacy and new simulation codes and uses a simple binomial model for the probability, {theta}, that, in an experiment chosen at random, the new code will provide a better prediction than the old. This model makes it possible to do statistical analysis with an absolute minimum of assumptions about the statistics of the quantities involved, at the price of discarding some potentially important information in the data. In particular, the analysis depends only on whether or not the new code predicts better than the old in any given experiment, and not on the magnitude of the improvement. We show how the posterior distribution for {theta} may be used, in a kind of Bayesian hypothesis testing, both to decide if an improvement has been observed and to quantify our confidence in that decision. We quantify the predictive probability that should be assigned, prior to taking any data, to the possibility of achieving a given level of confidence, as a function of sample size. We show how this predictive probability depends on the true value of {theta} and, in particular, how there will always be a region around {theta} = 1/2 where it is highly improbable that we will be able to identify an improvement in predictive capability, although the width of this region will shrink to zero as the sample size goes to infinity. We show how the posterior standard deviation may be used, as a kind of 'plan B metric' in the case that the analysis shows that {theta} is close to 1/2 and argue that such a plan B should generally be part of hypothesis testing. All the analysis presented in the paper is done with a general beta-function prior for {theta}, enabling sequential analysis in which a small number of new simulations may be done and the resulting posterior for {theta} used as a prior to inform the next stage of power analysis.

42 ENGINEERING↗

DOE OSTI2021

Beyond Taylor’s hypothesis: a novel volumetric reconstruction of velocity and density fields for variable‑density and shear flows

Abstract This work presents a novel numerical procedure for reconstructing volumetric density and velocity fields from planar laser-induced fluorescence (PLIF) and stereoscopic particle image velocimetry (SPIV) data. This new method is theoretically and practically demonstrated to provide more accurate 3D vortical structures and density fields in high shear flows than reconstruction methods based on the mean convective velocity. While Taylor’s hypothesis of frozen turbulence is commonly applied by using the local mean streamwise velocity, the proposed algorithm uses the measured local instantaneous velocity for data convection. It consists of a step-by-step reconstruction based on a mixed Lagrangian–Eulerian solver that includes the 3D interpolation of scattered flow data and that relaxes the Taylor’s hypothesis by iterative enforcement of the incompressibility constraint on the velocity field. This methodology provides 3D fields with temporal resolution, spatial resolution, and accuracy comparable to that of real 3D snapshots, thus providing a practical alternative to tomographic measurements. The procedure is validated using numerical data of the constant-density channel flow available on the Johns Hopkins University Turbulence Database (JHTDB), showing the accurate reconstruction of the 3D velocity field. The algorithm is applied to an experimental dataset of PLIF and SPIV measurements of a variable-density jet flow, demonstrating its capability to provide 3D velocity and density fields that are more consistent with the Navier–Stokes equations compared to the mean flow convective method. Graphic abstract

42 ENGINEERING↗

DOE OSTI2022

Hypothesis-Agnostic Network-Based Analysis of Real-World Data Suggests Ondansetron is Associated with Lower COVID-19 Any Cause Mortality

Background: The COVID-19 pandemic generated a massive amount of clinical data, which potentially hold yet undiscovered answers related to COVID-19 morbidity, mortality, long-term effects, and therapeutic solutions.Objectives: The objectives of this study were (1) to identify novel predictors of COVID-19 any cause mortality by employing artificial intelligence analytics on real-world data through a hypothesis-agnostic approach and (2) to determine if these effects are maintained after adjusting for potential confounders and to what degree they are moderated by other variables.Methods: A Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within the Interrogative Biology® platform was used for Bayesian network learning and hypothesis generation to analyze 16,277 PCR+ patients from a database of 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods during the first pandemic year in Central Florida. This approach generated Bayesian networks that enabled unbiased identification of significant predictors of any cause mortality for specific COVID-19 patient populations. These findings were further analyzed by logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping.Results: We found that in the COVID-19 PCR+ patient cohort, early use of the antiemetic agent ondansetron was associated with decreased any cause mortality 30 days post-PCR+ testing in mechanically ventilated patients.Conclusions: The results demonstrate how a real-world COVID-19-focused data analysis using artificial intelligence can generate unexpected yet valid insights that could possibly support clinical decision making and minimize the future loss of lives and resources.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Is Dose Deformation–Invariance Hypothesis Verified in Prostate IGRT?

Purpose: To assess dose uncertainties resulting from the dose deformation–invariance hypothesis in prostate cone beam computed tomography (CT)–based image guided radiation therapy (IGRT), namely to evaluate whether rigidly propagated planned dose distribution enables good estimation of fraction dose distributions. Methods and Materials: Twenty patients underwent a CT scan for planning intensity modulated radiation therapy–IGRT delivering 80 Gy to the prostate, followed by weekly CT scans. Two methods were used to obtain the dose distributions on the weekly CT scans: (1) recalculating the dose using the original treatment plan; and (2) rigidly propagating the planned dose distribution. The cumulative doses were then estimated in the organs at risk for each dose distribution by deformable image registration. The differences between recalculated and propagated doses were finally calculated for the fraction and the cumulative dose distributions, by use of per-voxel and dose-volume histogram (DVH) metrics. Results: For the fraction dose, the mean per-voxel absolute dose difference was <1 Gy for 98% and 95% of the fractions for the rectum and bladder, respectively. The maximum dose difference within 1 voxel reached, however, 7.4 Gy in the bladder and 8.0 Gy in the rectum. The mean dose differences were correlated with gas volume for the rectum and patient external contour variations for the bladder. The mean absolute differences for the considered volume receiving greater than or equal to dose x (V{sub x}) of the DVH were between 0.37% and 0.70% for the rectum and between 0.53% and 1.22% for the bladder. For the cumulative dose, the mean differences in the DVH were between 0.23% and 1.11% for the rectum and between 0.55% and 1.66% for the bladder. The largest dose difference was 6.86%, for bladder V{sub 80Gy}. The mean dose differences were <1.1 Gy for the rectum and <1 Gy for the bladder. Conclusions: The deformation–invariance hypothesis was corroborated for the organs at risk in prostate IGRT except in cases of a large disappearance or appearance of rectal gas for the rectum and large external contour variations for the bladder.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2026

Dara: Automated Multiple-Hypothesis Phase Identification and Refinement from Powder X-ray Diffraction

Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a single pattern, leading to potential misinterpretation when alternative solutions are overlooked. To ease humans’ efforts and address the challenge, we introduce Dara (data-driven automated Rietveld analysis), a framework designed to automate the robust identification and refinement of multiple phases from powder XRD data. Dara performs an exhaustive tree search over all plausible phase combinations within a given chemical space and validates each hypothesis using the BGMN Rietveld refinement routine. Key features include structural database filtering, automatic clustering of isostructural phases during tree expansion, and peak-matching-based scoring to identify promising phases for refinement. When ambiguity exists, Dara generates multiple hypothesis which can then be decided between by human experts or with further characterization tools. By enhancing the reliability and accuracy of phase identification, Dara enables scalable analysis of realistic complex XRD patterns and provides a foundation for integration into multimodal characterization workflows, moving toward fully self-driving materials discovery.

Biological databases↗

DOE OSTI2015

TESTS OF THE PLANETARY HYPOTHESIS FOR PTFO 8-8695b

The T Tauri star PTFO 8-8695 exhibits periodic fading events that have been interpreted as the transits of a giant planet on a precessing orbit. Here we present three tests of the planet hypothesis. First, we sought evidence for the secular changes in light-curve morphology that are predicted to be a consequence of orbital precession. We observed 28 fading events spread over several years and did not see the expected changes. Instead, we found that the fading events are not strictly periodic. Second, we attempted to detect the planet's radiation, based on infrared observations spanning the predicted times of occultations. We ruled out a signal of the expected amplitude. Third, we attempted to detect the Rossiter–McLaughlin effect by performing high-resolution spectroscopy throughout a fading event. No effect was seen at the expected level, ruling out most (but not all) possible orientations for the hypothetical planetary orbit. Our spectroscopy also revealed strong, time-variable, high-velocity Hα and Ca H and K emission features. All these observations cast doubt on the planetary hypothesis, and suggest instead that the fading events represent starspots, eclipses by circumstellar dust, or occultations of an accretion hotspot.

79 ASTRONOMY AND ASTROPHYSICS↗