19 research outputs found
How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning
Metamaterials are composite materials with engineered geometrical micro- and
meso-structures that can lead to uncommon physical properties, like negative
Poisson's ratio or ultra-low shear resistance. Periodic metamaterials are
composed of repeating unit-cells, and geometrical patterns within these
unit-cells influence the propagation of elastic or acoustic waves and control
dispersion. In this work, we develop a new interpretable, multi-resolution
machine learning framework for finding patterns in the unit-cells of materials
that reveal their dynamic properties. Specifically, we propose two new
interpretable representations of metamaterials, called shape-frequency features
and unit-cell templates. Machine learning models built using these feature
classes can accurately predict dynamic material properties. These feature
representations (particularly the unit-cell templates) have a useful property:
they can operate on designs of higher resolutions. By learning key coarse scale
patterns that can be reliably transferred to finer resolution design space via
the shape-frequency features or unit-cell templates, we can almost freely
design the fine resolution features of the unit-cell without changing coarse
scale physics. Through this multi-resolution approach, we are able to design
materials that possess target frequency ranges in which waves are allowed or
disallowed to propagate (frequency bandgaps). Our approach yields major
benefits: (1) unlike typical machine learning approaches to materials science,
our models are interpretable, (2) our approaches leverage multi-resolution
properties, and (3) our approach provides design flexibility.Comment: Under revie
Uncertainty Quantification of Bandgaps in Acoustic Metamaterials with Stochastic Geometric Defects and Material Properties
This paper studies the utility of techniques within uncertainty
quantification, namely spectral projection and polynomial chaos expansion, in
reducing sampling needs for characterizing acoustic metamaterial dispersion
band responses given stochastic material properties and geometric defects. A
novel method of encoding geometric defects in an interpretable, resolution
independent is showcased in the formation of input space probability
distributions. Orders of magnitude sampling reductions down to and
are achieved in the 1D and 7D input space scenarios respectively
while maintaining accurate output space probability distributions through
combining Monte Carlo, quadrature rule, and sparse grid sampling with surrogate
model fitting
Dolutegravir efficacy at 48 weeks in key subgroups of treatment-naive HIV-infected individuals in three randomized trials
Objectives:Dolutegravir (DTG) has been studied in three trials in HIV treatment-naive participants, showing noninferiority compared with raltegravir (RAL), and superiority compared with efavirenz and ritonavir-boosted darunavir. We explored factors that predicted treatment success, the consistency of observed treatment differences across subgroups and the impact of NRTI backbone on treatment outcome.Design:Retrospective exploratory analyses of data from three large, randomized, international comparative trials: SPRING-2, SINGLE, and FLAMINGO.Methods:We examined the efficacy of DTG in HIV-infected participants with respect to relevant demographic and HIV-1-related baseline characteristics using the primary efficacy endpoint from the studies (FDA snapshot) and secondary endpoints that examine specific elements of treatment response. Regression models were used to analyze pooled data from all three studies.Results:Snapshot response was affected by age, hepatitis co-infection, HIV risk factor, baseline CD4+ cell count, and HIV-1 RNA and by third agent. Differences between DTG and other third agents were generally consistent across these subgroups. There was no evidence of a difference in snapshot response between abacavir/lamivudine (ABC/3TC) and tenofovir/emtricitabine (TDF/FTC) overall [ABC/3TC 86%, TDF/FTC 85%, difference 1.1%, confidence interval (CI) â1.8, 4.0 percentage points, Pâ=â0.61] or at high viral loads (difference â2.5, 95% CI â8.9, 3.8 percentage points, Pâ=â0.42).Conclusions:DTG is a once-daily, unboosted integrase inhibitor that is effective in combination with either ABC/3TC or TDF/FTC for first-line antiretroviral therapy in HIV-positive individuals with a variety of baseline characteristics
Ten Years of Screening and Testing for Acute HIV Infection in North Carolina
To describe demographic and behavioral characteristics of persons with acute HIV infection (AHI) over time
Prediction of tensile performance for 3D printed photopolymer gyroid lattices using structural porosity, base material properties, and machine learning
Advancements in additive manufacturing (AM) technology and three-dimensional (3D) modeling software have enabled the fabrication of parts with combinations of properties that were impossible to achieve with traditional manufacturing techniques. Porous designs such as truss-based and sheet-based lattices have gained much attention in recent years due to their versatility. The multitude of lattice design possibilities, coupled with a growing list of available 3D printing materials, has provided a vast range of 3D printable structures that can be used to achieve desired performance. However, the process of computationally or experimentally evaluating many combinations of base material and lattice design for a given application is impractical. This research proposes a framework for quickly predicting key mechanical properties of 3D printed gyroid lattices using information about the base material and porosity of the structure. Experimental data was gathered to train a simple, interpretable, and accurate kernel ridge regression machine learning model. The performance of the model was then compared to numerical simulation data and demonstrated similar accuracy at a fraction of the computation time. Ultimately, the model development serves as an advancement in ML-driven mechanical property prediction that can be used to guide extension of current and future models
Polymer-graphite nanocomposites: Effective dispersion and major property enhancement via solid-state shear pulverization
More Narrative, Less Arithmetic: Appropriately Assessing the Harm of White-Collar Financial Losses
HIV-1 attachment inhibitor prodrug BMS-663068 in antiretroviral-experienced subjects: week 24 sub-group analysis
Dolutegravir efficacy at 48 weeks in key subgroups of treatment-naive HIV-infected individuals in three randomized trials
OBJECTIVES: Dolutegravir (DTG) has been studied in three trials in HIV treatment-naive participants, showing noninferiority compared with raltegravir (RAL), and superiority compared with efavirenz and ritonavir-boosted darunavir. We explored factors that predicted treatment success, the consistency of observed treatment differences across subgroups and the impact of NRTI backbone on treatment outcome. DESIGN: Retrospective exploratory analyses of data from three large, randomized, international comparative trials: SPRING-2, SINGLE, and FLAMINGO. METHODS: We examined the efficacy of DTG in HIV-infected participants with respect to relevant demographic and HIV-1-related baseline characteristics using the primary efficacy endpoint from the studies (FDA snapshot) and secondary endpoints that examine specific elements of treatment response. Regression models were used to analyze pooled data from all three studies. RESULTS: Snapshot response was affected by age, hepatitis co-infection, HIV risk factor, baseline CD4(+) cell count, and HIV-1 RNA and by third agent. Differences between DTG and other third agents were generally consistent across these subgroups. There was no evidence of a difference in snapshot response between abacavir/lamivudine (ABC/3TC) and tenofovir/emtricitabine (TDF/FTC) overall [ABC/3TC 86%, TDF/FTC 85%, difference 1.1%, confidence interval (CI) â1.8, 4.0 percentage points, Pâ=â0.61] or at high viral loads (difference â2.5, 95% CI â8.9, 3.8 percentage points, Pâ=â0.42). CONCLUSIONS: DTG is a once-daily, unboosted integrase inhibitor that is effective in combination with either ABC/3TC or TDF/FTC for first-line antiretroviral therapy in HIV-positive individuals with a variety of baseline characteristics