Error Model Research Articles

Recent Patents on Thermal Characteristic Analysis and Modeling of Machine Tools

Abstract: Modern machining machines are becoming more sophisticated and automated, but the problems of thermal deformation caused by machining have also come to the fore. CNC machine tools are used in high-speed, high-feed rate machining conditions, so the impact of thermal deformation factors will lead to a more prominent loss of accuracy. In this context, the design and manufacture of future CNC machine tools need to take further account of the errors caused by thermal deformation and the accompanying loss of accuracy. The principle of thermal characterisation and thermal error modeling of machine tools is to measure and reasonably analyse the machine tool at different temperature points and make corrections to the analysis data in order to get a value as close as possible to the real value and improve machining accuracy. Several representative patents and studies on thermal characterisation and thermal error modeling of machine tools at home and abroad are reviewed. This study presents a selection and summary of a large number of recent patents and studies on machine tool thermal characterisation and thermal error modeling, focusing on the selection of the machine tool thermal error measurement point area, thermal error modeling methods, spindle thermal displacement compensation, and other aspects, and discusses the future trends in the mainstream methods for reducing the thermal error of machine tools. The analysis and modeling of thermal errors in machine tools have an integral influence on further improvements in machining accuracy and efficiency in the future.

Investigating the influence of measurement uncertainty on chlorophyll-a predictions as an indicator of harmful algal blooms in machine learning models

Advancements in data availability, including high frequency, near real-time multiparameter sensors, laboratory analysis, and in-situ and remote observations, have driven the development of machine learning (ML) models for applications such as toxic Harmful Algal Bloom (HABs) monitoring. However, the performance of ML predictions is influenced by both model uncertainties due to inherent model structures and errors associated with input dataset measurements. For example, measurement uncertainty arises from sample collection, sensor drift and laboratory analysis and sample handling errors. While impacts of model uncertainty are commonly addressed using probabilistic approaches, the effect of measurement uncertainty is less studied due to the limited availability of detailed measurement information. This study focuses on assessing the impact of measurement uncertainty on the ML prediction of chlorophyll-a concentration as an index of HABs in a mesotrophic lake. Using randomized subsets of input measured datasets that mimic possible chlorophyll-a concentration distributions, the study built 1000 Random Forest (RF) and Support Vector Regression (SVR) models. An independent measured dataset was used to validate the ensemble models, allowing for model performance evaluation and the creation of prediction intervals to measure the propagated uncertainty. Our findings showed that the model predictions have MAE that ranged between 0.16 μg/l and 5.19 μg/l, and RMSE ranging between 0.20 μg/l and 7.39 μg/l. The highest uncertainty coverage of 0.71 was observed in the RF model without chlorophyll-a sensor values as a predictor. The study found that the training dataset sizes due to the high frequency and manually sampled nature influence how much measurement uncertainty is covered. The results of this study demonstrate how well ML models can capture various HABs patterns when given diverse measurement variables. Our findings will give researchers insightful information on how to lessen the impact of measurement uncertainty when using ML models as decision-support tools for HABs management.

The observation range adjusted method: a novel approach to accounting for observation uncertainty in model evaluation

Model evaluations are performed by comparing a modelled quantity with an observation of that quantity and any deviation from this observed quantity is considered an error. We know that all observing systems have uncertainties, and multiple observational products for the same quantity can provide equally plausible ‘truths’. Thus, model errors depend on the choice of observation used in the evaluation exercise. We propose a method that considers models to be indistinguishable from observations when they lie within the range of observations, and hence are not assigned any error. Errors are assigned when models are outside the observational range. Errors calculated in this way can be used within traditional statistics to calculate the Observation Range Adjusted (ORA) version of that statistic. The ORA statistics highlight the measurable errors of models, provide more robust model performance rankings, and identify areas of the model where further model development is likely to lead to consistent model improvements.

Numerical modeling on wave–current flows and bed shear stresses over an algal reef

AbstractCurrents in coastal zones under multiple mechanisms in terms of tides, waves, wind, and high roughness are difficult to model; bed shear stresses under wave–current flows are particularly challenging yet not being well studied. Few studies reported the modeling and validation of the bed shear stress in reef environments. In this paper, we present the first direct assessment of numerical modeling on depth-averaged currents and bed shear stresses over an algal reef using a coupled wave–current model (Delft-3D). The modeled results were validated and compared to the field observed data. The model considers hydrodynamic forcing in terms of tides, waves, wind stresses, and bed friction. Results show that the model generally reproduces the depth-averaged currents and bed shear stresses when considering all the mechanisms. Two numerical cases with and without wind forcing were tested to examine the effects of the winds. We found that the tide is mostly the primary factor driving the current, even in shallow waters within a depth of 3 m; however, the currents are also significantly affected by wind speeds and wind directions during high-wind events. When the wind direction is in the same direction as the tidal current, the current speed increases, suggesting the importance of the wind stress on the coastal currents. In addition, two models were chosen to study the nonlinear enhancement of bed shear stress by waves. We found a significant difference between the two models in predicting the bed shear stresses compared to the observed data. Nonlinear contribution from wave enhances the magnitude of bed shear stresses, which reduces the model error. The results highlight the nonlinear interaction between waves and currents is meaningful in predicting the bed shear stresses during high-wave-orbital motions; improvement of the present wave-current nonlinear interaction model for predicting the bed shear stresses may be needed.

Transmembrane protein 53 craniotubular dysplasia (OMIM # 619727): The skeletal disease and consequent blindness of this new disorder

Craniotubular dysplasia, Ikegawa type (OMIM #619727) denotes the autosomal recessive skeletal disease identified in 2021 featuring blindness acquired in childhood. Five young members of four Indian families harbored a homozygous indel within TMEM53 (OMIM *619722), the gene that encodes transmembrane protein 53 (TMEM53). When intact, TMEM53 spans the nuclear envelope of osteoprogenitor cells, dampens BMP-SMAD signaling, and thereby slows bone formation. Consequently, defective TMEM53 accelerates osteogenesis. Herein, an American boy is compound heterozygous for a novel deletion and a novel missense mutation within TMEM53. His vision and sensorineural hearing became impaired. Radiographic survey revealed diploic thickening of his skull, broad calvarial and facial bones, skeletal modeling errors, vertebral body flattening, wide ribs, and osteopenia of expanded bones. DXA areal bone density (gm/cm2) Z-scores were low. His optic, auditory, and spinal canals were narrow. Mineral metabolism was intact. Serum alkaline phosphatase and osteocalcin levels were normal yet CTX was high. Iliac crest histomorphometry documented accelerated bone formation. His acute vision loss briefly improved following prednisone administration, optic canal decompression, and optic nerve sheath fenestration, but then progressed despite further surgeries and zoledronate treatment aimed to suppress bone turnover. Next generation sequencing of genes associated with elevated skeletal mass, including from high bone turnover, did not suggest an etiology. Whole genome sequencing then revealed within TMEM53: i) a paternally transmitted 54-base deletion, which included the mRNA splice acceptor site for exon 2 as well as 31 bases of exonic sequence (c. 62-23_92del), and ii) a maternally transmitted missense variant (c.650C > T, p.Ser217Leu: NM_024587.4/NP_078863.2) which is extremely rare in gnomAD (frequency = 0.000036), replaces Ser217 highly conserved across species, and is scored as damaging by SIFT and Mutation Taster. We call this new osteopathy TMEM53 craniotubular dysplasia.

Twinning quality sensors in wastewater treatment process via optimized echo state network-based soft sensors

The presence of a large amount of quality-related but hard-to-measure variables usually makes effective monitoring of industrial processes difficult, and even impossible. Soft computing techniques and digital twins can revolutionize standard approaches to solve this issue, even though industrial data are frustrated by nonlinearity, disturbances, and dynamic behaviors. In this light, this paper proposes a data-driven soft sensor with the help of the Fourier amplitude sensitivity test (FAST) and improved probabilistic regularized echo state network (IPRESN), then to twin the quality hardware sensors. Within this framework, the initial step is to rely on the FAST method to measure the collaborated importance of multiple process variables with respect to each quality variable. This quantification facilitates the selection of auxiliary variables for the training of IPRESN. Furthermore, a probabilistic regularization method is learned to improve the robust performance of the echo state network (ESN) by re-designing a new objective function to minimize the mean and variance of the modeling error. Additionally, the whale optimization algorithm (WOA) is improved and then used to optimize the critical hyper-parameters of ESN, thus mitigating the risks associated with suboptimal hyperparameter selection. The effectiveness of the proposed method is verified through a Benchmark Simulation Model 2 (BSM2) and a full-scale wastewater treatment plant data set. The findings show the potential of the proposed method in facilitating digital twin implementations for hardware sensors.

Modeling of deposition morphology and characteristic dimensions in material extrusion additive manufacturing

Developing prediction models for deposition strand morphology is critical in Material Extrusion Additive Manufacturing (MEAM) to optimize processes and guide innovative deposition strategies, while advancing our understanding of the flow mechanics during the deposition process.This study aimed to develop an accurate, rapid, and calibration-free model for predicting deposition strand morphology, height, and width. The relationship between the flow field constraints downstream of the nozzle and the deposition morphology was analyzed. By solving the flow front boundary in the region covered by the nozzle, a model for predicting the deposition height and width under constrained conditions was developed. Moreover, a quantitative method for predicting morphological features was offered. The established model is validated through fused deposition modeling (FDM) single-layer single-path experiments, demonstrating superior accuracy. An in-depth analysis of the model errors resulting from the assumptions in the model has been carried out.This work provided deeper insights by modeling the flow front boundary, which helps to understand the distribution of pressure flow and drag flow during the deposition process. The established model has better application potential due to its advantage of not requiring prior calibration of the machine.

Effect of Tripping and Domain Width on Transonic Buffet on Periodic NASA-CRM Airfoils

Transonic buffet is an instability characterized by shock oscillations and separated boundary layers. High-fidelity simulations have typically been limited to narrow domains to be computationally feasible, overly constraining the flow and introducing modeling errors. Depending on the boundary-layer state upstream of the interaction, different buffet features are observed. High-fidelity simulations (implicit large-eddy simulation) were performed on the periodic (infinite) NASA-CRM wing at a moderate Reynolds number to assess the sensitivity of the two-dimensional transonic buffet to boundary-layer state and domain width. Simulations were cross-validated against low-fidelity Reynolds-averaged Navier–Stokes (RANS)/unsteady RANS and global stability analysis, and excellent agreement was found near the onset. By varying the boundary-layer tripping amplitude, laminar, transitional, and turbulent buffet interactions were obtained. All cases consisted of a single shock and low-frequency oscillations (St≈0.07). The transitional interaction also exhibited reduced shock movement, a 15% increase in CL¯, and energy content at higher frequencies (St≈1.3). Spanwise domain studies showed sensitivity at the shock location and near the trailing edge. We conclude that the span width must be greater than the trailing-edge boundary-layer thickness to obtain span-independent solutions. For largely separated cases, the sensitivity to span width increased, and variations across the span were observed. This was found to be associated with a loss of two-dimensionality in the flow.

A data model to forecast the morphological evolution of multiple beach profiles

Beaches are a natural defense against extreme events, such as storms and hurricanes, whose intensity and frequency are expected to increase in the future due to climate change. In this context, models that forecast the morphological evolution of coastal areas can be used to anticipate the effects of future scenarios, allowing early action to mitigate the damage caused by extreme events. Hence, this study included data from three different monitoring programs in data models to simulate the seasonal morphological evolution of several Portuguese beaches. Two different data models were implemented using the Random Forest algorithm. One was fed with profile data and wave conditions while the other considered also sediment size data. Both models achieved suitable performances, but the inclusion of sediment data reduced the model errors and variance, and thus improved model performance. It was demonstrated that combining data from multidisciplinary campaigns can be a solution to generate reliable and robust morphological forecasting models.

Reliability framework for CLT floors in out-of-plane bending using Monte Carlo simulation

Sustainably sourced engineered wood products (EWP) such as cross-laminated timber (CLT) floor panels are viable alternatives to conventional concrete and steel, however their inherent natural variability increases perceived risks and limits wide-spread adoption. The reliability index (β) is the metric used by design standards to communicate allowable structural risk. Although not included directly in any structural capacity equations, it governs all the partial resistance factors and load amplification factors that inform those design code calculations. CLT floors have significant partial resistance factors due to the natural variability of the material that effectively penalise the calculated design strength of those products. By exploring their reliability there is the potential to allow these products to be designed more efficiently, leading to increased competitiveness among traditional materials and less waste. A novel statistical capacity prediction model that goes beyond a simple averaged-value strength assumption to include consideration of the variability between individual boards within a panel was validated against experimental data. A CLT database of 237 out-of-plane bending test results was collected for the first time to inform an appropriate model error factor. Monte Carlo simulation (MCS) approaches were adopted to predict the reliability levels of CLT panels by defining the material, geometrical and loading parameters as random variables and assigning each a mean, coefficient of variation and appropriate distribution. The target and calculated reliability levels were compared across both Eurocode 5 and the North American codes ANSI/APA PRG 320 and CSA O86 as the preeminent CLT design codes internationally. This study found that the calculated reliability levels met or exceeded both codes in most scenarios. The impacts of width, load ratio, material strength distribution, and CLT layups on the expected failure probabilities were explored with sensitivity analyses. The MCS framework was also applied to the calibration of an appropriate resistance factor for CLT design to inform the potential creation of an Australian CLT design code. A resistance factor of 0.85 was calibrated which falls between the equivalent European and North American code resistance factors.

Spatiotemporal Variability of Channel Roughness and its Substantial Impacts on Flood Modeling Errors

AbstractManning's roughness coefficient, n, is used to describe channel roughness, and is a widely sought‐after key parameter for estimating and predicting flood propagation. Due to its control of flow velocity and shear stress, n is critical for modeling timing of floods and pollutants, aquatic ecosystem health, infrastructural safety, and so on. While alternative formulations exist, open‐channel n is typically regarded as temporally constant, determined from lookup tables or calibration, and its spatiotemporal variability was never examined holistically at large scales. Here, we developed and analyzed a continental‐scale n dataset (along with alternative formulations) calculated from observed velocity, slope, and hydraulic radius in 200,000 surveys conducted over 5,000 U.S. sites. These large, diverse observations allowed training of a Random Forest (RF) model capable of predicting n (or alternative parameters) at high accuracy (Nash Sutcliffe model efficiency >0.7) in space and time. We show that predictable time variability explains a large fraction (∼35%) of n variance compared to spatial variability (50%). While exceptions abound, n is generally lower and more stable under higher streamflow conditions. Other factorial influences on n including land cover, sinuosity, and particle sizes largely agree with conventional intuition. Accounting for temporal variability in n could lead to substantially larger (45% at the median site) estimated flow velocities under high‐flow conditions or lower (44%) velocities under low‐flow conditions. Habitual exclusion of n temporal dynamics means flood peaks could arrive days before model‐predicted flood waves, and peak magnitude estimation might also be erroneous. We therefore offer a model of great practical utility.

Two-dimensional iterative learning hybrid robust fuzzy predictive control for nonlinear multi-phase batch processes with asynchronous switching

For nonlinear multi-phase batch processes with uncertainties, time-varying delays, and unknown bounded disturbances, an iterative learning hybrid robust fuzzy predictive control approach is developed within the context of two-dimensional systems. First, a two-dimensional T-S fuzzy Fornasini-Marchesini comprehensive feedback error model is constructed, which includes a state increment and an output error. Based on the established model and considering an asynchronous switching situation between a controller and a system state, a feedback error switching model, including match and mismatch cases, is developed. Then, a two-dimensional iterative learning hybrid robust fuzzy predictive controller is built, based on the established switching model. Second, building on related theories and methodologies, the stability conditions are derived by a linear matrix inequality form, while the system's asymptotic and exponential stability are analyzed. Next, the stability conditions are calculated to get the control law gains, minimum and maximum dwelling times. The acquired gains of control law greatly minimize the controller's learning cycle, while the advanced switching signal based on the obtained dwelling times is dynamically adjusted to ensure a smooth transition and the system's stability during switching. Ultimately, a simulation case is utilized to validate the proposed method's effectiveness and feasibility.

Error analysis and calibration based on division-of-aperture bionic polarization navigation systems

Bionic polarization navigation technology has become an important navigation technology due to its non-dependence on satellite technology, and strong anti-jamming ability. However, in practical applications, the performance parameters of the structure of division-of-aperture polarization navigation systems limit the accuracy of polarization navigation. Therefore, this paper investigates the measurement error model and the corresponding calibration method for the multi-channel structure of division-of-aperture polarization navigation systems. In this paper, we first establish a mathematical model between the error of skylight polarization information and the performance parameters of navigation systems for the unique multi-channel structure of division-of-aperture polarization navigation systems. Secondly, for the first time, a mathematical model is used to quantitatively analyze and give the performance parameters suitable for the acquisition of skylight polarization information (polarizer installation errors ≤2°, grayscale non-uniformity ≤10(DN)). Then, a polarizer installation errors calibration algorithm based on Singular Value Decomposition (SVD) with linear fitting for grayscale non-uniformity response calibration algorithm is proposed and validated through indoor and outdoor calibration experiments. The experimental results show that the azimuth accuracy after calibration of polarizer installation errors can reach 0.382°, and the azimuth accuracy after calibration of grayscale non-uniformity response can reach 0.101°.

Soil organic carbon sequestration potential explained by mineralogical and microbiological activity using spectral transfer functions

The ability of soil to sequester carbon and reduce atmospheric CO2 concentrations is limited and depends on the soil minerals and their interaction with the microbiota. Microbial activities are closely associated with the types and amounts of soil organic matter (SOM) and clay minerals that have functional groups that interact with energy in Vis NIR-SWIR and Mid-IR wavelengths. The main objective of this research was to determine, based on these spectral ranges, the relation between mineralogical and organic compounds, as their sequestration and specialization in soils from Brazil. It was possible to map microbiological activity by spectral transfer functions and digital soil mapping reaching R2 from 0.77 to 0.85. Multiple regression equations were constructed to quantify enzymatic activity, microbial biomass carbon (MBC), particulate organic matter (POM), and resistant forms of carbon, and SOM associated with the mineral fraction (MAOM). All these properties were detected by specific bands obtained with the recursive feature elimination (RFE) algorithm, reaching correlations from 0.64 to 0.98 in specific ranges. The prediction model of the carbon sequestration potential was adjusted with microbiological and mineralogical variables from Vis-NIR-SWIR and the Mid-IR spectral range. A SARAR double autoregressive model was adjusted with r 0.61 and to a spatial error model (SEM) with r 0.7. The explanatory variables were associated with kaolinite, hematite, goethite, gibbsite, and the abundance of fungi, actinomycetes, vesico-arbuscular mycorrhizal fungi, enzymatic activity of beta-glucosidase, urease and phosphatase, and POM. Among the microbiological variables, the general abundance of fungi was the most important, in contrast to enzymatic activity that was the least important. The interaction between the different maps constructed and historical land use allowed the identification of areas that contribute to sequestering new carbon and could be the key to climate change mitigation strategies.

Establishing On-Wafer Calibration Standards for the 16-Term Error Model: Application to Silicon High-Frequency Transistor Characterization

Establishing On-Wafer Calibration Standards for the 16-Term Error Model: Application to Silicon High-Frequency Transistor Characterization

DeTELpy: Python package for high-throughput detection of amino acid substitutions in mass spectrometry datasets.

Errors in the processing of genetic information during protein synthesis can lead to phenotypic mutations, such as amino acid substitutions, e.g. by transcription or translation errors. While genetic mutations can be readily identified using DNA sequencing, and mutations due to transcription errors by RNA sequencing, translation errors can only be identified proteome-wide using mass spectrometry. Here, we provide a Python package implementation of a high-throughput pipeline to detect amino acid substitutions in mass spectrometry datasets. Our tools enable users to process hundreds of mass spectrometry datasets in batch mode to detect amino acid substitutions and calculate codon-specific and site-specific translation error rates. deTELpy will facilitate the systematic understanding of amino acid misincorporation rates (translation error rates), and the inference of error models across organisms and under stress conditions, such as drug treatment or disease conditions. deTELpy is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://git.mpi-cbg.de/tothpetroczylab/detelpy and https://pypi.org/project/deTELpy/ and can be easily installed via pip install deTELpy.

Accuracy of US CDC COVID-19 forecasting models.

Accurate predictive modeling of pandemics is essential for optimally distributing biomedical resources and setting policy. Dozens of case prediction models have been proposed but their accuracy over time and by model type remains unclear. In this study, we systematically analyze all US CDC COVID-19 forecasting models, by first categorizing them and then calculating their mean absolute percent error, both wave-wise and on the complete timeline. We compare their estimates to government-reported case numbers, one another, as well as two baseline models wherein case counts remain static or follow a simple linear trend. The comparison reveals that around two-thirds of models fail to outperform a simple static case baseline and one-third fail to outperform a simple linear trend forecast. A wave-by-wave comparison of models revealed that no overall modeling approach was superior to others, including ensemble models and errors in modeling have increased over time during the pandemic. This study raises concerns about hosting these models on official public platforms of health organizations including the US CDC which risks giving them an official imprimatur and when utilized to formulate policy. By offering a universal evaluation method for pandemic forecasting models, we expect this study to serve as the starting point for the development of more accurate models.

A Grain Number Counting Method Based on Image Characteristic Parameters of Wheat Spikes

In order to measure wheat yield and wheat spike phenotypes, the grain number of wheat spikes is counted manually at present, but acquiring the grain number of wheat spikes is laborious and time-consuming. Counting the grain number of wheat spikes with an image processing method is promising, yet the application of this method is flawed due to its low accuracy. In this work, images of wheat spikes were collected and processed with technical procedures, including image cropping, image graying, histogram equalization, image binarization, eroding operation, removing small objects, filling image holes, revolving vertical spikes, cutting off stems, and removing stems. Wheat stems in binary images were eliminated by the sum pixels method, and the morphological characteristic parameters of the image areas of wheat spikes and lengths of wheat spike axes were calculated. Mathematical models relating the image areas of wheat spikes and lengths of the wheat spike axes to the grain number were established, and the mathematical models were verified. The results showed that the characteristic parameters of the image areas of wheat spikes and the lengths of the wheat spike axes for the spike images were linear relative to the grain number, and the maximum determination coefficients R2 were 0.9336 and 0.9012, respectively. The maximum determination coefficients R2 for the practical and predicted grain numbers were 0.9552 and 0.9369, respectively, and the minimum average absolute error was 2.3, while the average relative error for the mathematical models was 5.65%. The mathematical models relating the image areas of wheat spikes and the lengths of the wheat spike axes to the grain number were practical and accurate, and the mathematical model comparing the image area of wheat spikes and the grain number was superior to that comparing the length of the wheat spike axis and the grain number. The grain number of wheat spikes could be acquired accurately and quickly by the image processing method extracting the characteristic parameters of wheat spikes.

Anisotropic tomography of eastern Tibet and its uncertainty from hypocentral errors

SUMMARY The mechanism responsible for the lateral expansion and uplift of the eastern Tibetan Plateau remains a topic of ongoing debate, partly due to discrepancies in the results of seismic velocity and anisotropy. In local earthquake tomography, hypocentral uncertainties can cause significant errors in the tomographic model. However, this issue has received limited attention in previous studies. In this work, we employ the weighted least-squares (WLS) method to solve the tomographic inversion problem. A power exponent coefficient, which is called weighting level, is introduced into the weighting matrix to control the relative contribution of the data with different hypocentral errors to the final tomographic result. Our data set contains high-quality Pg, Pn and Sg arrival times of local earthquakes recorded by the dense Chinese seismic network in eastern Tibet during 2008–2022. We comprehensively analyse the inversion results derived from the WLS inversions with different weighting levels to evaluate the robustness of isotropic velocity anomalies and azimuthal anisotropy. The most robust feature of our results is a striking low-velocity (low-Vp) zone surrounded by high-velocity (high-Vp) anomalies and fault parallel fast-velocity directions (FVDs) of azimuthal anisotropy in the lower crust beneath the western side of the Longmenshan fault zone. Taking into account many previous results of the region, we deem that the low-Vp zone reflects hot and wet upwelling flow from the deep asthenosphere, which ascends to the lower crust along the fault zone. At the NE margin of the Tibetan Plateau, significant low-Vp anomalies exist in the lower crust and the FVDs are consistent with the motion direction of the Tibetan block revealed by GPS (Global Positioning System) observations. We think that lower crustal flow exists beneath NE Tibet, which controls the plateau expansion toward the northeast. A low-Vp anomaly appears at 30 km depth beneath the Sichuan Basin. However, as the weighting level increases, the amplitude of this low-Vp anomaly decreases by more than 6 per cent, suggesting that this low-Vp anomaly has a lower accuracy than the other features.

Robust Quantum Gates against Correlated Noise in Integrated Quantum Chips.

As quantum circuits become more integrated and complex, additional error sources that were previously insignificant start to emerge. Consequently, the fidelity of quantum gates benchmarked under pristine conditions falls short of predicting their performance in realistic circuits. To overcome this problem, we must improve their robustness against pertinent error models besides isolated fidelity. Here, we report the experimental realization of robust quantum gates in superconducting quantum circuits based on a geometric framework for diagnosing and correcting various gate errors. Using quantum process tomography and randomized benchmarking, we demonstrate robust single-qubit gates against quasistatic noise and spatially correlated noise in a broad range of strengths, which are common sources of coherent errors in large-scale quantum circuits. We also apply our method to nonstatic noises and to realize robust two-qubit gates. Our Letter provides a versatile toolbox for achieving noise-resilient complex quantum circuits.