924 research outputs found
Failure-informed adaptive sampling for PINNs
Physics-informed neural networks (PINNs) have emerged as an effective
technique for solving PDEs in a wide range of domains. It is noticed, however,
the performance of PINNs can vary dramatically with different sampling
procedures. For instance, a fixed set of (prior chosen) training points may
fail to capture the effective solution region (especially for problems with
singularities). To overcome this issue, we present in this work an adaptive
strategy, termed the failure-informed PINNs (FI-PINNs), which is inspired by
the viewpoint of reliability analysis. The key idea is to define an effective
failure probability based on the residual, and then, with the aim of placing
more samples in the failure region, the FI-PINNs employs a failure-informed
enrichment technique to adaptively add new collocation points to the training
set, such that the numerical accuracy is dramatically improved. In short,
similar as adaptive finite element methods, the proposed FI-PINNs adopts the
failure probability as the posterior error indicator to generate new training
points. We prove rigorous error bounds of FI-PINNs and illustrate its
performance through several problems.Comment: 21 pages, 18 figure
ISBORD: Internet Searching Based on Resource Description
Based on the Information-Centric Networking (ICN) concept and the mature model of the current TCP/IP-based Internet, we propose content searching based on universal and scalable resource description, namely ISBORD (Internet Searching based on Resource Description). This novel concept aims to improve the efficiency of content searching and simplifies the end-user functionality to support the evolution of the content-centric Internet
Adaptive operator learning for infinite-dimensional Bayesian inverse problems
The fundamental computational issues in Bayesian inverse problems (BIPs)
governed by partial differential equations (PDEs) stem from the requirement of
repeated forward model evaluations. A popular strategy to reduce such cost is
to replace expensive model simulations by computationally efficient
approximations using operator learning, motivated by recent progresses in deep
learning. However, using the approximated model directly may introduce a
modeling error, exacerbating the already ill-posedness of inverse problems.
Thus, balancing between accuracy and efficiency is essential for the effective
implementation of such approaches. To this end, we develop an adaptive operator
learning framework that can reduce modeling error gradually by forcing the
surrogate to be accurate in local areas. This is accomplished by fine-tuning
the pre-trained approximate model during the inversion process with adaptive
points selected by a greedy algorithm, which requires only a few forward model
evaluations. To validate our approach, we adopt DeepOnet to construct the
surrogate and use unscented Kalman inversion (UKI) to approximate the solution
of BIPs, respectively. Furthermore, we present rigorous convergence guarantee
in the linear case using the framework of UKI. We test the approach on several
benchmarks, including the Darcy flow, the heat source inversion problem, and
the reaction diffusion problems. Numerical results demonstrate that our method
can significantly reduce computational costs while maintaining inversion
accuracy
Unseen Image Synthesis with Diffusion Models
While the current trend in the generative field is scaling up towards larger
models and more training data for generalized domain representations, we go the
opposite direction in this work by synthesizing unseen domain images without
additional training. We do so via latent sampling and geometric optimization
using pre-trained and frozen Denoising Diffusion Probabilistic Models (DDPMs)
on single-domain datasets. Our key observation is that DDPMs pre-trained even
just on single-domain images are already equipped with sufficient
representation abilities to reconstruct arbitrary images from the inverted
latent encoding following bi-directional deterministic diffusion and denoising
trajectories. This motivates us to investigate the statistical and geometric
behaviors of the Out-Of-Distribution (OOD) samples from unseen image domains in
the latent spaces along the denoising chain. Notably, we theoretically and
empirically show that the inverted OOD samples also establish Gaussians that
are distinguishable from the original In-Domain (ID) samples in the
intermediate latent spaces, which allows us to sample from them directly.
Geometrical domain-specific and model-dependent information of the unseen
subspace (e.g., sample-wise distance and angles) is used to further optimize
the sampled OOD latent encodings from the estimated Gaussian prior. We conduct
extensive analysis and experiments using pre-trained diffusion models (DDPM,
iDDPM) on different datasets (AFHQ, CelebA-HQ, LSUN-Church, and LSUN-Bedroom),
proving the effectiveness of this novel perspective to explore and re-think the
diffusion models' data synthesis generalization ability.Comment: 28 pages including appendice
Boundary Guided Mixing Trajectory for Semantic Control with Diffusion Models
Applying powerful generative denoising diffusion models (DDMs) for downstream
tasks such as image semantic editing usually requires either fine-tuning
pre-trained DDMs or learning auxiliary editing networks. In this work, we
achieve SOTA semantic control performance on various application settings by
optimizing the denoising trajectory solely via frozen DDMs. As one of the first
optimization-based diffusion editing work, we start by seeking a more
comprehensive understanding of the intermediate high-dimensional latent spaces
by theoretically and empirically analyzing their probabilistic and geometric
behaviors in the Markov chain. We then propose to further explore the critical
step in the denoising trajectory that characterizes the convergence of a
pre-trained DDM. Last but not least, we further present our method to search
for the semantic subspaces boundaries for controllable manipulation, by guiding
the denoising trajectory towards the targeted boundary at the critical
convergent step. We conduct extensive experiments on various DPMs architectures
(DDPM, iDDPM) and datasets (CelebA, CelebA-HQ, LSUN-church, LSUN-bedroom,
AFHQ-dog) with different resolutions (64, 256) as empirical demonstrations.Comment: 24 pages including appendices, code will be available at
https://github.com/L-YeZhu/BoundaryDiffusio
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