Physics-Informed Regularization of Deep Neural Networks

This paper presents a novel physics-informed regularization method for training of deep neural networks (DNNs). In particular, we focus on the DNN representation for the response of a physical or biological system, for which a set of governing laws are known. These laws often appear in the form of differential equations, derived from first principles, empirically-validated laws, and/or domain expertise. We propose a DNN training approach that utilizes these known differential equations in addition to the measurement data, by introducing a penalty term to the training loss function to penalize divergence form the governing laws. Through three numerical examples, we will show that the proposed regularization produces surrogates that are physically interpretable with smaller generalization errors, when compared to other common regularization methods

FO-PINNs: A First-Order formulation for Physics Informed Neural Networks

We present FO-PINNs, physics-informed neural networks that are trained using the first-order formulation of the Partial Differential Equation (PDE) losses. We show that FO-PINNs offer significantly higher accuracy in solving parameterized systems compared to traditional PINNs, and reduce time-per-iteration by removing the extra backpropagations needed to compute the second or higher-order derivatives. Additionally, unlike standard PINNs, FO-PINNs can be used with exact imposition of boundary conditions using approximate distance functions, and can be trained using Automatic Mixed Precision (AMP) to further speed up the training. Through two Helmholtz and Navier-Stokes examples, we demonstrate the advantages of FO-PINNs over traditional PINNs in terms of accuracy and training speedup.Comment: 6 pages, 3 figures, Selected for ML4PS workshop at NeurIPS 202

Biopathologic Characterization of Three Mixed Poultry Eimeria spp. Isolates

Background: Coccidiosis of domestic fowl, caused by species of the Genus Eimeria, is responsi­ble for important economic losses in poultry production. Because different species and/or strains can vary in pathogenicity and other biological parameters, their precise characteriza­tion is important for epizootiological studies.Methods: Fifty samples from litter, whole intestinal tract and feces were collected from poul­try houses located in different provinces of Iran. One hundred twenty male day-old broi­ler chicks were challenged with three selected isolates. Data on weight gain, Food Conversion Ratio (FCR), food intake, lesion scoring and shedding of oocysts per gram of feces were rec­orded and analyzed by the Duncan's test.Results: In all treatments, the challenged groups had statistically significant lower weight gain than that of unchallenged control group. Isolate three caused the lowest weight gain and food intake and the worst lesion score as well as FCR. Despite originating from close geographi­cal regions for isolates 1 and 2, the difference in biopathologic factors may be either due to different proportion of identified species or the different pathogenicity of the species present in the isolates.Conclusion: The results highlight the importance of considering various species of Eimeria in designing the preventive, control and treatment strategies to prevent coccidiosis in different regions of Iran. Further characterization of each isolate would be the next step to provide a basis for coccidiosis research with well-characterized local isolates

First Detection of Nosema ceranae, a Microsporidian Protozoa of European Honeybees (Apis mellifera) In Iran

Background: Nosemosis of European honey bee (Apis mellifera) is present in bee colonies world­wide. Until recently, Nosema apis had been regarded as the causative agent of the disease, that causes heavy economic losses in apicultures. Nosema ceranae is an emerging microsporidian para­site of European honeybees, A. mellifera, but its distribution is not well known. Previously, nosemosis in honeybees in Iran was attributed exclusively to N. apis.Methods: Six Nosema positive samples (determined from light microscopy of spores) of adult worker bees from one province of Iran (Savadkouh- Mazandaran, northern Iran) were tested to determine Nosema species using previously- developed PCR primers of the 16 S rRNA gene. As it is difficult to distinguish N. ceranae and N. apis morphologically, a PCR assay based on 16 S ribosomal RNA has been used to differentiate N. apis and N. ceranae.Results: Only N. ceranae was found in all samples, indicating that this species present in Iran apiar­ies.Conclusion: This is the first report of N. ceranae in colonies of A. mellifera in Iran. It seems that intensive surveys are needed to determine the distribution and prevalence of N. ceranae in differ­ent regions of Iran