Fredrik Lindgren

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We consider a semilinear parabolic PDE driven by additive noise. The equation is discretized in space by a standard piecewise linear finite element method. We show that the orthogonal expansion of the finite-dimensional Wiener process, that appears in the discretized problem, can be truncated severely without losing the asymptotic order of the method,(More)
Multivariate PCA- and PLS-models involving many variables are often difficult to interpret, because plots and lists of loadings, coefficients, VIPs, etc, rapidly become messy and hard to overview. There may then be a strong temptation to eliminate variables to obtain a smaller data set. Such a reduction of variables, however, often removes information and(More)
Acute pancreatitis (AP) is now well recognized as a possible complication of childhood cancer treatment, interrupting the chemotherapy regimen, and requiring prolonged hospitalization, possibly with intensive care and surgical intervention, thereby compromising the effect of chemotherapy and the remission of the underlying malignant disease. This review(More)
Autosomal recessive mutations in LRBA, encoding for LPS-responsive beige-like anchor protein, were described in patients with a common variable immunodeficiency (CVID)-like disease characterized by hypogammaglobulinemia, autoimmune cytopenias, and enteropathy. Here, we detail the clinical, immunological, and genetic features of a patient with severe(More)
We consider the stochastic Allen-Cahn equation perturbed by smooth additive Gaussian noise in a spatial domain with smooth boundary in dimension d ≤ 3, and study the semidiscretization in time of the equation by an implicit Euler method. We show that the method converges pathwise with a rate O(∆t γ) for any γ < 1 2. We also prove that the scheme converges(More)
We study linear stochastic evolution partial differential equations driven by additive noise. We present a general and flexible framework for representing the infinite dimensional Wiener process which is driving the equation. Since the eigenfunctions and eigenvalues of the covariance operator of the process are usually not available for computations, we(More)
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