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1. A blob method for inhomogeneous diffusion with applications to multi-agent control and sampling

   https://doi.org/10.1090/mcom/3841  

   Craig, Katy; Elamvazhuthi, Karthik; Haberland, Matt; Turanova, Olga (November 2023, Mathematics of Computation)

   As a counterpoint to classical stochastic particle methods for linear diffusion equations, such as Langevin dynamics for the Fokker-Planck equation, we develop a deterministic particle method for the weighted porous medium equation and prove its convergence on bounded time intervals. This generalizes related work on blob methods for unweighted porous medium equations. From a numerical analysis perspective, our method has several advantages: it is meshfree, preserves the gradient flow structure of the underlying PDE, converges in arbitrary dimension, and captures the correct asymptotic behavior in simulations. 

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2. From low to high-and lower-optimal regularity of the SMGTJ equation with Dirichlet and Neumann boundary control, and with point control, via explicit representation formulae

   https://doi.org/10.3934/eect.2022007  

   Triggiani, Roberto; Wan, Xiang (January 2022, Evolution Equations and Control Theory)

   We consider the linear third order (in time) PDE known as the SMGTJ-equation, defined on a bounded domain, under the action of either Dirichlet or Neumann boundary control \begin{document}$ g $$\end{document}. Optimal interior and boundary regularity results were given in [1], after [41], when \begin{document}$$ g \in L^2(0, T;L^2(\Gamma)) \equiv L^2(\Sigma) $$\end{document}, which, moreover, in the canonical case \begin{document}$$ \gamma = 0 $$\end{document}, were expressed by the well-known explicit representation formulae of the wave equation in terms of cosine/sine operators [19], [17], [24,Vol Ⅱ]. The interior or boundary regularity theory is however the same, whether \begin{document}$$ \gamma = 0 $$\end{document} or \begin{document}$$ 0 \neq \gamma \in L^{\infty}(\Omega) $$\end{document}, since \begin{document}$$ \gamma \neq 0 $$\end{document} is responsible only for lower order terms. Here we exploit such cosine operator based-explicit representation formulae to provide optimal interior and boundary regularity results with \begin{document}$$ g $$\end{document} "smoother" than \begin{document}$$ L^2(\Sigma) $$\end{document}, qualitatively by one unit, two units, etc. in the Dirichlet boundary case. To this end, we invoke the corresponding results for wave equations, as in [17]. Similarly for the Neumann boundary case, by invoking the corresponding results for the wave equation as in [22], [23], [37] for control smoother than \begin{document}$$ L^2(0, T;L^2(\Gamma)) $$\end{document}, and [44] for control less regular in space than \begin{document}$$ L^2(\Gamma) $$\end{document}$. In addition, we provide optimal interior and boundary regularity results when the SMGTJ equation is subject to interior point control, by invoking the corresponding wave equations results [42], [24,Section 9.8.2]. 

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3. Effect of ultrasound amplitude and frequency on nanoparticle diffusion in an agarose hydrogel

   https://doi.org/10.1121/10.0012972  

   Karki, Alina; Marshall, Jeffrey S.; Wu, Junru (July 2022, The Journal of the Acoustical Society of America)

   Exposure of nanoparticles in a porous medium, such as a hydrogel, to low-intensity ultrasound has been observed to dramatically enhance particle penetration rate. Enhancement of nanoparticle penetration is a key issue affecting applications such as biofilm mitigation and targeted drug delivery in human tissue. The current study used fluorescent imaging to obtain detailed experimental measurements of the effect of ultrasound amplitude and frequency on diffusion of nanoparticles of different diameters in an agarose hydrogel, which is often used as a simulant for biofilms and biological tissues. We demonstrate that the acoustic enhancement occurs via the phenomenon of oscillatory diffusion, in which a combination of an oscillatory flow together with random hindering of the particles by interaction with hydrogel proteins induces a stochastic random walk of the particles. The measured variation of acoustic diffusion coefficients with amplitude and frequency were used to validate a previous statistical theory of oscillatory diffusion based on the continuous time random walk approach. 

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4. Beta Diffusion

   Zhou, Mingyuan; Chen, Tianqi; Wang, Zhendong; Zheng, Huangjie (December 2023, Neural Information Processing Systems)

   We introduce beta diffusion, a novel generative modeling method that integrates demasking and denoising to generate data within bounded ranges. Using scaled and shifted beta distributions, beta diffusion utilizes multiplicative transitions over time to create both forward and reverse diffusion processes, maintaining beta distributions in both the forward marginals and the reverse conditionals, given the data at any point in time. Unlike traditional diffusion-based generative models relying on additive Gaussian noise and reweighted evidence lower bounds (ELBOs), beta diffusion is multiplicative and optimized with KL-divergence upper bounds (KLUBs) derived from the convexity of the KL divergence. We demonstrate that the proposed KLUBs are more effective for optimizing beta diffusion compared to negative ELBOs, which can also be derived as the KLUBs of the same KL divergence with its two arguments swapped. The loss function of beta diffusion, expressed in terms of Bregman divergence, further supports the efficacy of KLUBs for optimization. Experimental results on both synthetic data and natural images demonstrate the unique capabilities of beta diffusion in generative modeling of range-bounded data and validate the effectiveness of KLUBs in optimizing diffusion models, thereby making them valuable additions to the family of diffusion-based generative models and the optimization techniques used to train them. 

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5. Exact QR Factorizations of Rectangular Matrices

   https://doi.org/10.1007/s11590-024-02095-z  

   Lourenco, Christopher; Moreno-Centeno, Erick (April 2024, Optimization Letters)

   QR factorization is a key tool in mathematics, computer science, operations research, and engineering. This paper presents the roundoff-error-free (REF) QR factorization framework comprising integer-preserving versions of the standard and the thin QR factorizations and associated algorithms to compute them. Specifically, the standard REF QR factorization factors a given matrix $$A \in \Z^{m \times n}$$ as $A=QDR$, where $$Q \in \Z^{m \times m}$$ has pairwise orthogonal columns, $$D$$ is a diagonal matrix, and $$R \in \Z^{m \times n}$$ is an upper trapezoidal matrix; notably, the entries of $$Q$$ and $$R$$ are integral, while the entries of $$D$$ are reciprocals of integers. In the thin REF QR factorization, $$Q \in \Z^{m \times n}$$ also has pairwise orthogonal columns, and $$R \in \Z^{n \times n}$$ is also an upper triangular matrix. In contrast to traditional (i.e., floating-point) QR factorizations, every operation used to compute these factors is integral; thus, REF QR is guaranteed to be an exact orthogonal decomposition. Importantly, the bit-length of every entry in the REF QR factorizations (and within the algorithms to compute them) is bounded polynomially. Notable applications of our REF QR factorizations include finding exact least squares or exact basic solutions (i.e., a rational n-dimensional vector $$x$$) to any given full column rank or rank deficient linear system $A x = b$, respectively. In addition, our exact factorizations can be used as a subroutine within exact and/or high-precision quadratic programming. Altogether, REF QR provides a framework to obtain exact orthogonal factorizations of any rational matrix (as any rational/decimal matrix can be easily transformed into an integral matrix). 

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