Explainable Artificial Intelligence for Mechanics: Physics-Explaining Neural Networks for Constitutive Models

(Artificial) neural networks have become increasingly popular in mechanics and materials sciences to accelerate computations with model order reduction techniques and as universal models for a wide variety of materials. However, the major disadvantage of neural networks remains: their numerous parameters are challenging to interpret and explain. Thus, neural networks are often labeled as black boxes, and their results often elude human interpretation. The new and active field of physics-informed neural networks attempts to mitigate this disadvantage by designing deep neural networks on the basis of mechanical knowledge. By using this a priori knowledge, deeper and more complex neural networks became feasible, since the mechanical assumptions can be explained. However, the internal reasoning and explanation of neural network parameters remain mysterious. Complementary to the physics-informed approach, we propose a first step towards a physics-explaining approach, which interprets neural networks trained on mechanical data a posteriori. This proof-of-concept explainable artificial intelligence approach aims at elucidating the black box of neural networks and their high-dimensional representations. Therein, the principal component analysis decorrelates the distributed representations in cell states of RNNs and allows the comparison to known and fundamental functions. The novel approach is supported by a systematic hyperparameter search strategy that identifies the best neural network architectures and training parameters. The findings of three case studies on fundamental constitutive models (hyperelasticity, elastoplasticity, and viscoelasticity) imply that the proposed strategy can help identify numerical and analytical closed-form solutions to characterize new materials

An efficient order reduction strategy in earthquake nonlinear response analysis of structures

Since earthquake dynamic response analysis of large and complex structures are computationally time demanding, efficient methods that can reduce the system order are of high interest. In this sense, there are different methods available, which try to provide a proper equivalent model. However, in the presence of nonlinearities in the structural elements, most of those methods are ruled out due to their linear assumptions. Therefore, this contribution aims at providing an efficient strategy, which can reduce the order of the nonlinear structural model while retaining important structural characteristics for further earthquake dynamic response analysis. The model order reduction (MOR) strategy is developed based on the proper orthogonal decomposition (POD) method to derive a set of nonlinear deterministic POD modes according to the information of the response history (snapshots) of the full order structure under one or a set of representative earthquake excitations. Subsequently, the POD modes are utilized to create the reduced-order models of the structure subjected to different earthquake excitations. Then, the reduced order models need substantially less amount of computational time in comparison to the full order models. This study presents the application results of the introduced new strategy to a realistic building structure, which is base-isolated by means of frictional bearing elements for better seismic performance. The results demonstrate accurate approximations of the physical (full) responses by means of this new MOR strategy if the probable behavior of the structure has already been captured in the POD snapshots

Development and validation of a new self-report measure of pain behaviors

Pain behaviors that are maintained beyond the acute stage post-injury can contribute to subsequent psychosocial and physical disability. Critical to the study of pain behaviors is the availability of psychometrically sound pain behavior measures. In this study we developed a self-report measure of pain behaviors, the Pain Behaviors Self Report (PaB-SR). PaB-SR scores were developed using item response theory and evaluated using a rigorous, multiple-witness approach to validity testing. Participants included: a) 661 survey participants with chronic pain and with multiple sclerosis (MS), back pain, or arthritis; b) 618 survey participants who were significant others of a chronic pain participant; and c) 86 participants in a videotaped pain behavior observation protocol. Scores on the PaB-SR were found to be measurement invariant with respect to clinical condition. PaB-SR scores, observer-reports, and the video-taped protocol yielded distinct, but convergent views of pain behavior, supporting the validity of the new measure. The PaB-SR is expected to be of substantial utility to researchers wishing to explore the relationship between pain behaviors and constructs such as pain intensity, pain interference, and disability

Patient-orientated longitudinal study of multiple sclerosis in south west England (The South West Impact of Multiple Sclerosis Project, SWIMS) 1: protocol and baseline characteristics of cohort

<p>Abstract</p> <p>Background</p> <p>There is a need for greater understanding of the impact of multiple sclerosis (MS) from the perspective of individuals with the condition. The South West Impact of MS Project (SWIMS) has been designed to improve understanding of disease impact using a patient-centred approach. The purpose is to (1) develop improved measurement instruments for clinical trials, (2) evaluate longitudinal performance of a variety of patient-reported outcome measures, (3) develop prognostic predictors for use in individualising drug treatment for patients, particularly early on in the disease course.</p> <p>Methods</p> <p>This is a patient-centred, prospective, longitudinal study of multiple sclerosis and clinically isolated syndrome (CIS) in south west England. The study area comprises two counties with a population of approximately 1.7 million and an estimated 1,800 cases of MS. Self-completion questionnaires are administered to participants every six months (for people with MS) or 12 months (CIS). Here we present descriptive statistics of the baseline data provided by 967 participants with MS.</p> <p>Results</p> <p>Seventy-five percent of those approached consented to participate. The male:female ratio was 1.00:3.01 (n = 967). Average (standard deviation) age at time of entry to SWIMS was 51.6 (11.5) years (n = 961) and median (interquartile range) time since first symptom was 13.3 (6.8 to 24.5) years (n = 934). Fatigue was the most commonly reported symptom, with 80% of participants experiencing fatigue at baseline. Although medication use for symptom control was common, there was little evidence of effectiveness, particularly for fatigue. Nineteen percent of participants were unable to classify their subtype of MS. When patient-reported subtype was compared to neurologist assessment for a sample of participants (n = 396), agreement in disease sub-type was achieved in 63% of cases. There were 836 relapses, reported by 931 participants, in the twelve months prior to baseline. Twenty-three percent of the relapsing-remitting group and 12% of the total sample were receiving disease-modifying therapy at baseline.</p> <p>Conclusions</p> <p>Demographics of this sample were similar to published data for the UK. Overall, the results broadly reflect clinical experience in confirming high symptom prevalence, with relatively little complete symptom relief. Participants often had difficulty in defining MS relapses and their own MS type.</p